Abstract: In order to reduce the impact of uncertainty on project performance objectives and improve the robustness of scheduling, the discrete time-cost tradeoff problem for repetitive projects with fuzzy activity durations is investigated. A fuzzy chance-constrained programming model considering the risk preferences of decision makers is developed by means of fuzzy risk measurement to determine the optimal execution modes for all activities (i.e., mode list), thereby to minimize the project budget while meeting the pre-specified risk levels of project delays and cost overruns. Given a known mode list, a forward recursive process for calculating the membership functions of fuzzy project duration and fuzzy total cost is proposed, while an improved genetic algorithm based on electromagnetic mechanism (GA-EM) for searching the optimal mode list is designed accordingly. The effectiveness of the algorithm is verified using a real-life engineering case, and the computational performance of the algorithm is analyzed via numerical experiments. Results show that GA-EM can provide a fuzzy schedule that satisfies the given levels of schedule delays and cost overrun risks, with the average and maximum percentage deviations in the budget not exceeding 0.096% and 0.239%, respectively.