Together with the particle swarm optimization algorithm, a physical programming-based multi-objective optimization model is developed to seek the optimal strategy for spare parts arrangement. The levels of satisfaction and the preference functions as well as the aggregate objective function for the supportability are designed which can reflect the preference of decision makers. With the proposed optimization method, the computational burden in large-scale multi-objective design problems can be greatly reduced. Meanwhile, the results from the proposed method are compared with that of single-objective optimization, demonstrating the effectiveness of the proposed model.