In order to solve the multi-objective optimization of structures with constrains, the immune clonal selection algorithm was applied. Based on the immunology theory, the non-dominated neighbor-based selection, proportional cloning and elitism strategy were introduced in the multi-objective immune clonal selection algorithm (MOICSA) to enhance the diversity, the uniformity and the convergence of the solution obtained. Penalty function method was used to deal with violated constraints. Several classical problems were solved to demonstrate the feasibility and effectiveness of the MOICSA algorithm, and the results were compared with other optimization methods. The simulation results show that the algorithm has advantages in convergence speed, time consuming and solution quality.