Most of the existing multi-objective optimization algorithms focused on how to transform the multi-objectives into single objective, while little consideration was given on how to deal with constraint condition. To deal with this problem, an improved ant colony optimization algorithm is proposed in this paper. Logistic chaos is used to initialize the group, which makes the pheromone concentration difference on each path at the initial time, provided direction guidance for ants at the beginning of searching and improved search efficiency. Furthermore, the idea of Pareto sorting is used to sort the feasible solution, and get the Pareto solution set. The numerical experiments show that the proposed algorithm achieves significantly better performance than the others on most of the tested problems, which indicates the superiority of the proposed algorithm for solving constrained multi-objective optimization problems (CMOPs).