Multi-objective optimization problems are crucial in real-world scenarios, where multiple solutions exist rather than a single one. Traditional methods like PERT/CPM often struggle to address such problems effectively. Meta- heuristic techniques, such as genetic algorithms and non-dominated sorting genetic algorithms (NSGA-II), are well- suited for finding true Pareto-optimal solutions. This paper introduces an enhanced NSGA-II algorithm, which utilizes Sobol sequences for initial population generation, ensuring uniform search space coverage and faster convergence. The proposed algorithm is validated using benchmark problems from the ZDT test suite and compared with state-of-the- art algorithms. Additionally, real-world optimization problems in project management, particularly the time-cost trade- off (TCT) problem, are solved using the enhanced NSGA-II. The performance evaluation includes key metrics such as standard deviation, providing a comprehensive assessment of the algorithm’s efficiency. Experimental results confirm that the proposed method outperforms traditional NSGA-II and other meta-heuristic algorithms in maintaining a well- distributed Pareto front while ensuring computational efficiency.