Abstract: The solution of constrained multi-objective optimization problems aims to reasonably allocate limited search resources to the satisfaction of constraints and the optimization of objective functions. However, the increasing complexity of problem constraints has brought great challenges to the solution algorithm. Aiming at the above challenges, this thesis puts forward an adaptive constrained multi-objective co-evolutionary algorithm(ACMCA). The algorithm evolves two populations (the main population and the archive population) with complementary functions simultaneously, so that the algorithm can achieve a good balance between constraint processing and objective optimization when solving complex constraint problems. Firstly, the main population carries out dual reproduction. In the first reproduction process, the valuable information carried by the infeasible solution is adaptively used through the dynamic fitness distribution function, so that the population emphasizes the optimization of the objective function in the early stage of evolution and the feasibility in the later stage. The second reproduction cooperates with the archived population to improve the convergence of the population and maintain diversity. Then, an angle-based selection scheme is proposed to update the archived population, which ensures the good diversity of the population while maintaining the search pressure of the population to the Pareto front. Lastly, the algorithm conducts comparison experiments with five advanced constrained multi-objective evolutionary algorithms on 33 benchmark problems, and the test results demonstrate that the proposed algorithm is more advantageous than the comparison algorithms in dealing with various types of constrained multi-objective optimization problems, and its average efficiency is improved by about 67%.