Abstract: In order to cope with uncertain disturbance encountered in the construction process of repetitive projects, proactive scheduling optimization of repetitive projects is necessary to increase the capacity of project schedules to accommodate uncertainties. In this paper, the float relationships among linear, bar and block activities in repetitive projects is first analyzed based on linear planning. The selection ranges between buffers are determined according to floats. Then, a multi-objective proactive scheduling model is established with the objectives of project duration, cost and robustness. To address its NP-hard nature, a modified particle swarm optimization algorithm (i.e., SA-PSOc) is designed, which incorporate simulated annealing algorithm to avoid particles easily falling into local optima. Algorithm testing shows that the modified algorithm has better global search ability and faster computational speed. Finally, the feasibility and effectiveness of the proposed proactive scheduling optimization model are verified by a case study of a repetitive project. Through simulation, it demonstrates that the proposed method consumes fewer buffers to ensure the project completion on time. Conclusion is made that when preparing a schedule, by increasing resources within a certain range, a better schedule can be achieve.