The time-cost trade-off problem is a known bi-objective problem in the field of project management. Recently, a new parameter, the quality of the project has been added to previously considered time and cost parameters. The main specification of the time-cost trade-off problem is discretization of the decision space to limited and accountable decision variables. In this situation the efficiency of the traditional methods decrease and applying of the evolutionary algorithms is necessary. In this paper, two evolutionary algorithms that originally search the decision space in a continuous manner including: (1) multi-objective particle swarm optimization (MOPSO) and (2) nondominated sorting genetic algorithm (NSGA)-II, are considered as the optimization tools to solve two construction project management problems. These problems are both in discrete domain including two or tree objectives, separately. In this regard, some procedures has been suggested and then applied to adopt both algorithms capable in solving the problems in a discrete domain. Results show the advantages and effectiveness of the used procedures in reporting the optimal Pareto for the optimization problems. Moreover, the NSGA-II is more successful in determining optimal alternatives in both time-cost trade-off (TCTO) and time-cost-quality trade-off (TCQTO) problems than the MOPSO algorithm.
