The present study proposes a bi-objective particle swarm optimization algorithm to find the cost profile over the set of feasible project durations, i.e., the time-cost tradeoff analysis. The proposed algorithm aims to achieve two goals: (1) to obtain the entire Pareto front in a single run, and (2) to treat various types of activity time-cost functions, such as linear, nonlinear, discrete, discontinuous, and a hybrid of the above. The proposed algorithm adopts an elite archiving scheme to store nondominated solutions and to direct further search. Through a fast food outlet example, the proposed algorithm is shown effective and efficient in conducting bi-objective time-cost analysis. The proposed algorithm has also been shown competitive with SPEA2 by achieving better solution diversity.