In view of the problem that it is difficult for constrained multi-objective optimization algorithms to effectively balance convergence and diversity, a new constrained multi-objective optimization algorithm based on coevolution was proposed. Firstly, a population with certain number of feasible solutions was obtained by using the feasible solution search method based on steady-state evolution. Then, this population was divided into two sub-populations and both convergence and diversity were achieved by coevolution of the two sub-populations. Finally, standard constrained multi-objective optimization problems CF1~CF7, DOC1~DOC7 and the practical engineering problems were used for simulation experiments to test the solution performance of the proposed algorithm. Experimental results show that compared with Nondominated Sorting Genetic Algorithm Ⅱ based on Constrained Dominance Principle (NSGA-Ⅱ-CDP), Two-Phase algorithm (ToP), Push and Pull Search algorithm (PPS) and Two-Archive Evolutionary Algorithm for Constrained multiobjective optimization (C-TAEA), the proposed algorithm achives good results in both Inverted Generational Distance (IGD) and HyperVolume (HV), indicating that the proposed algorithm can effectively balance convergence and diversity.