This paper presents a multi-objective optimization model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows (GVRP-FTW). The model allows customers to select multiple time windows for deliveries, ranked by preference, to enhance flexibility and efficiency in route planning. The optimization aims to minimize tour costs, promote electromobility over fossil fuels, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives: overall cost, use of fossil fuel, and customer satisfaction. Four mainstream solvers—NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA—are applied to approximate the Pareto front for the problem. The study uses five different single-vehicle route planning problems and finds that the selection of the metaheuristic significantly affects algorithm performance. The resulting 3-D Pareto fronts reveal that most users can still be delivered in their most preferred time windows with only small concessions to other objectives. However, using only one time window per user can lead to increasingly drastic cost and fossil fuel consumption. The paper contributes by proposing an innovative scheme involving multiple time windows and modeling this scheme in a multi-objective route planning problem with hybrid vehicles. The study also analyzes the trade-offs between the three goals of eco-friendly, cost-efficient, and customer-satisfactory last-mile logistics. Computational results demonstrate the feasibility and advantages of implementing this multiple time window scheme. The paper includes a comprehensive literature review, problem description, and computational experiments, highlighting the importance of considering environmental impact, customer satisfaction, and economic cost in last-mile logistics. The study proposes a three-objective route planning model to obtain Pareto optimal solutions, minimizing economic cost, reducing environmental impact, and maximizing customer satisfaction. The model is formulated with parameters and variables, and constraints are introduced to ensure that customers are only visited once and to handle the switch between diesel and electricity based on travel distance. The study also evaluates the effectiveness of different algorithms in solving the problem and provides insights into the trade-offs between the objectives. The results show that the proposed model and algorithms are effective in solving the GVRP-FTW problem and provide valuable insights for optimizing last-mile logistics.This paper presents a multi-objective optimization model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows (GVRP-FTW). The model allows customers to select multiple time windows for deliveries, ranked by preference, to enhance flexibility and efficiency in route planning. The optimization aims to minimize tour costs, promote electromobility over fossil fuels, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives: overall cost, use of fossil fuel, and customer satisfaction. Four mainstream solvers—NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA—are applied to approximate the Pareto front for the problem. The study uses five different single-vehicle route planning problems and finds that the selection of the metaheuristic significantly affects algorithm performance. The resulting 3-D Pareto fronts reveal that most users can still be delivered in their most preferred time windows with only small concessions to other objectives. However, using only one time window per user can lead to increasingly drastic cost and fossil fuel consumption. The paper contributes by proposing an innovative scheme involving multiple time windows and modeling this scheme in a multi-objective route planning problem with hybrid vehicles. The study also analyzes the trade-offs between the three goals of eco-friendly, cost-efficient, and customer-satisfactory last-mile logistics. Computational results demonstrate the feasibility and advantages of implementing this multiple time window scheme. The paper includes a comprehensive literature review, problem description, and computational experiments, highlighting the importance of considering environmental impact, customer satisfaction, and economic cost in last-mile logistics. The study proposes a three-objective route planning model to obtain Pareto optimal solutions, minimizing economic cost, reducing environmental impact, and maximizing customer satisfaction. The model is formulated with parameters and variables, and constraints are introduced to ensure that customers are only visited once and to handle the switch between diesel and electricity based on travel distance. The study also evaluates the effectiveness of different algorithms in solving the problem and provides insights into the trade-offs between the objectives. The results show that the proposed model and algorithms are effective in solving the GVRP-FTW problem and provide valuable insights for optimizing last-mile logistics.