In order to improve the distribution and convergence of the non-dominated solution set of multi-objective optimization problem, according to the characteristics of different differential evolution strategies, a novel decomposition multi-objective evolution algorithm based on Chebyshev decomposition mechanism and differential evolution model with multi-strategy, namely MOEA/D-WMSDE is proposed in this paper. The MOEA/D-WMSDE uses Chebyshev decomposition mechanism to transform multi-objective optimization problem into a series of single objective optimization subproblems. Then the wavelet basis function and normal distribution are used to control parameters. A new optimal mutation strategy based on complementary advantages of five mutation strategies is deeply studied in order to propose a new differential evolution (WMSDE) algorithm with multi-strategy. On this basis, a new MOEA/D-WMSDE algorithm is realized. Finally, ZDT and DTLZ benchmark functions are used to prove the optimization performance of the MOEA/D-WMSDE. The experimental results show that the MOEA/D-WMSDE has greatly improved the convergence and distribution, and can effectively solve the multi-objective optimization problem. Compared with the other algorithms, the overall quality of the obtained solution set is best among these algorithms. This study provides a new method to solve multi-objective optimization problem.