This paper proposes a modified immune clonal constrained multi-objective algorithm for constrained multi-objective optimization problems. By introducing a new constrained handling strategy to modify the objective values of individuals, the proposed algorithm optimizes the individuals with the modified objective values and stores the non-dominated feasible individuals in an elitist population. In the optimization process, the algorithm not only preserves the non-dominated feasible individuals, but also utilizes the infeasible solutions with smaller constrained violation values. Meanwhile the new algorithm introduces the overall cloning strategy to improve the distribution diversity of the solutions. The proposed algorithm has been tested on several popular constrained test problems and compared with the other two constrained multi-objective optimization algorithms. The results show that the optimal solutions of the proposed algorithm are more diverse than the other two algorithms and better in terms of convergence and uniformity.