In this paper, the disadvantages of some existing algorithms in handling constrained objective problems (COPs) are analyzed and an algorithm used for COPs—immune clonal multi-objective optimization algorithm (ICMOA) is proposed. This algorithm treats constrained optimization as a multi-objective optimization with two objectives. One objective is the original objective function and the other is obtained by the constraints. The concept of the Pareto-dominance in multi-objective optimization is introduced and each individual is implemented clone, mutation, selection and other operations based on the degree of its Pareto-dominance. The clone operation implements the searching for optimal solution in the global region and is available for getting a high quality solution. The mutation operation improves the searching for optimal solution in the local region and assures the diversity of the solutions. The selection operation guarantees the convergence to the optimal solution and improves the convergence speed. Based on the theorem of Markov chain, the global convergence of the new algorithm is proved. Compared with the existing algorithms, simulation results on 13 benchmark test problems show that the new algorithm has some advantages in convergence speed and precision.