This study presents a time-cost trade-off (TCT) optimization model using multi-objective particle swarm optimization (MOPSO) to address the challenges of balancing time and cost in construction projects. The model focuses on multi-mode construction activities, where each mode represents a different option for a construction activity requiring varying resources, time, and cost. The goal is to select the best alternatives for project activities. The model is applied to a real case study project to demonstrate its effectiveness in generating a set of Pareto-optimal solutions. The proposed method is evaluated against existing trade-off optimization methods to assess its ability to simultaneously optimize time and cost. Project teams are provided with trade-off plots and an a priori approach to select one of the generated Pareto-optimal solutions. The research benefits project stakeholders by maximizing profits and assists organizations and project teams in improving scheduling choices. Construction projects are critical for national development and require careful planning, scheduling, and control. Time and cost are conflicting objectives, and achieving a balance is essential for effective project execution. Recent research has focused on resolving trade-off issues, with various methods such as deterministic, heuristic, and metaheuristic approaches being used. The study proposes a novel model for optimizing TCT issues to relieve the complexity and constraints of current models. The paper thoroughly examines existing literature, identifies research gaps, and details the developed MOPSO model with implementation using MATLAB software. The efficacy and practicality of the model are demonstrated through comparative analysis and application in a case study project. The findings show the effectiveness of the proposed model in optimizing time and cost, and the study outlines avenues for future research.This study presents a time-cost trade-off (TCT) optimization model using multi-objective particle swarm optimization (MOPSO) to address the challenges of balancing time and cost in construction projects. The model focuses on multi-mode construction activities, where each mode represents a different option for a construction activity requiring varying resources, time, and cost. The goal is to select the best alternatives for project activities. The model is applied to a real case study project to demonstrate its effectiveness in generating a set of Pareto-optimal solutions. The proposed method is evaluated against existing trade-off optimization methods to assess its ability to simultaneously optimize time and cost. Project teams are provided with trade-off plots and an a priori approach to select one of the generated Pareto-optimal solutions. The research benefits project stakeholders by maximizing profits and assists organizations and project teams in improving scheduling choices. Construction projects are critical for national development and require careful planning, scheduling, and control. Time and cost are conflicting objectives, and achieving a balance is essential for effective project execution. Recent research has focused on resolving trade-off issues, with various methods such as deterministic, heuristic, and metaheuristic approaches being used. The study proposes a novel model for optimizing TCT issues to relieve the complexity and constraints of current models. The paper thoroughly examines existing literature, identifies research gaps, and details the developed MOPSO model with implementation using MATLAB software. The efficacy and practicality of the model are demonstrated through comparative analysis and application in a case study project. The findings show the effectiveness of the proposed model in optimizing time and cost, and the study outlines avenues for future research.