project-oriented organizations, aiming to select an optimal combination of projects under resource constraints and conflicting objectives. This study models the project portfolio selection problem as a multi-objective optimization issue, where economic, social, and environmental performance indicators are maximized while the risk indicator is minimized. This approach enables simultaneous consideration of sustainable development aspects and risk management. In this research, a multi-objective genetic algorithm was employed to generate the Pareto front (set of non-dominated solutions) in order to effectively identify trade-offs among the different objectives. Subsequently, a multi-criteria weighting method was applied based on the decision-maker’s preferences to select a final optimal portfolio from the Pareto solutions. Additionally, the Pareto front was visualized using multi-dimensional scatter plots to facilitate visual analysis of the balance between objectives for decision-makers. The results revealed that the “Gachsaran 1 and 2” portfolio, with approximate values (Economic = 0.80, Social = 0.60, Environmental = 0.50, Risk = 0.20), emerged as a non-dominated solution with very low risk, making it suitable for conservative strategies. In contrast, the multi-criteria weighting method selected the “Nargesi” portfolio, with values (Economic = 0.90, Social = 0.70, Environmental = 0.60, Risk = 0.30), as the superior option, reflecting a priority on economic return and acceptance of moderate risk. The integration of metaheuristic algorithms with multi-criteria decision-making methods provides an efficient and flexible framework for optimizing project portfolio management.