The Weapon Target Assignment (WTA) problem is a fundamental optimization problem that seeks to determine the optimal allocation of weapons to targets to maximize specific performance indicators. This paper addresses the WTA problem from a multi-objective optimization perspective, incorporating three key objectives: target damage probability, strike balance, and strike benefit. To effectively tackle this problem, a multi-operator evolutionary algorithm is proposed, integrating various evolutionary operators to enhance both exploration and exploitation capabilities within the solution space. Furthermore, a dynamic adaptive strategy is introduced to adjust the selection of operators based on their performance during the optimization process. In the experimental evaluation, nine multi-objective WTA instances of varying scales are generated to assess the performance of the proposed algorithm. The results are compared against five state-of-the-art multi-objective optimization algorithms. Experimental findings demonstrate that the proposed algorithm exhibits strong competitiveness in solving the multi-objective WTA problem, outperforming or matching the performance of existing approaches.