Considering the high computational cost in multi-objective simulation optimization and the difficulty of obtaining black box function, a multi-objective parallel surrogate-based optimization method based on dual weighted constraint expectation improvement strategy is proposed. Firstly, Kriging model is established to estimate the prediction uncertainty of untested points; Secondly, the dual weighted constraint expectation improvement strategy is constructed, and the new strategy is integrated by infill strategy matrix and distance aggregation method; Then, the integration strategy is maximized to realize multi-objective parallel optimization; Finally, the Pareto optimal solution set is obtained when the termination condition reached. Test functions and pinned-pinned sandwich beam design cases are employed for optimization verification. Comparison and optimization results show that the proposed method can effectively improve the efficiency of multi-objective optimization. Compared with similar methods, the optimization results in low dimensional problems have better convergence, diversity and distribution.