Intensive heuristic and metaheuristic research efforts have focused on the Pareto front optimization of discrete time-cost trade-off problem (DTCTP). However, very little success has been achieved in solving the problem for medium and large-scale projects. This paper presents a new particle swarm optimization method to achieve an advancement in the Pareto front optimization of medium and large-scale construction projects. The proposed Pareto front particle swarm optimizer (PFPSO) is based on a multiobjective optimization environment with novel particle representation, initialization, and position-updating principles that are specifically designed for simultaneous time-cost optimization of large-scale projects. PFPSO brings several benefits for the discrete time-cost optimization, such as an adequate representation of the discrete search space, fast convergence properties, and improved Pareto front optimization capabilities. The computational experiment results reveal that the new particle swarm optimization method outperforms the state-of-the-art methods, both in terms of the number of Pareto front solutions and computation time, especially for medium and large-scale problems. A large number of nondominated solutions are achieved within seconds for the first time, for a problem including 720 activities. The proposed Pareto front particle swarm optimizer provides a fast and effective method for optimal scheduling of construction projects. (C) 2016 American Society of Civil Engineers.