Summary: This study addresses one of the most challenging issues in designing a sustainable and efficient ride-sharing service. This paper uses an extensive computational study to quantify the behavior of carpooling in customized bus routing problems. This mechanism allows organizations to draw on the potential of their employees’ private cars to provide convenient alternative rides for other employees, thereby reducing air pollution and greenhouse gas emissions as well as increasing overall satisfaction with the transportation system offered. The objective functions minimize: \((i)\) total transportation costs and incentives paid to drivers of private cars, (ii) dissatisfaction as determined by staff walking distance, travel time, and delays in arriving at work, and (iii) total carbon emissions generated by commuting. We propose a resolution algorithm based on Pareto Strength Ant Colony Optimization (PSACO) as an effective meta-heuristic method for solving the multi-objective mathematical model and compare it with the results obtained by an exact method. The effectiveness and applicability of the proposed problem have been evaluated by performing computational experiments on a real case study in Paris using a number of comparative metrics with appropriate assumptions. Different parameters affecting the performance of the algorithm are also investigated. The concluding section presents a comparison of the results achieved. The test outcomes confirm that the formulation and the solution methods can be useful references for practice. The insights obtained from the research could provide the basis for designing incentive schemes and information campaigns aimed at making ride-sharing systems more successful and improving their performance.