This paper presents a multi-objective optimization model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows (GVRPTW). The study aims to reduce tour costs, promote the use of electric vehicles over diesel, and meet customer preferences. Customers are asked to provide alternative time windows, ranked in order of preference, to offer flexibility and help route planners find more fuel-efficient routes. The optimization model incorporates 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. The computational results demonstrate that the selection of the meta-heuristic solver significantly impacts algorithm performance. The 3-D Pareto fronts reveal that most users can still be delivered within their preferred time windows with minimal concessions to other objectives. However, using only one time window per user can lead to higher costs and fossil fuel consumption. The study contributes to the literature by proposing an innovative scheme involving multiple time windows and modeling it in a multi-objective route planning problem with hybrid vehicles.This paper presents a multi-objective optimization model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows (GVRPTW). The study aims to reduce tour costs, promote the use of electric vehicles over diesel, and meet customer preferences. Customers are asked to provide alternative time windows, ranked in order of preference, to offer flexibility and help route planners find more fuel-efficient routes. The optimization model incorporates 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. The computational results demonstrate that the selection of the meta-heuristic solver significantly impacts algorithm performance. The 3-D Pareto fronts reveal that most users can still be delivered within their preferred time windows with minimal concessions to other objectives. However, using only one time window per user can lead to higher costs and fossil fuel consumption. The study contributes to the literature by proposing an innovative scheme involving multiple time windows and modeling it in a multi-objective route planning problem with hybrid vehicles.