This thesis is devoted to developing an efficient multiobjective optimization algorithm based on artificial immune systems. Multiobjective optimization is a very important research topic both in science and engineering since the presence of multiple and often competing objectives is natural in most real-world problems. In most cases, multi objective optimization problems are difficult to solve because the multiple objectives often conflict across a complex and high-dimension search space. Therefore, efficient multiobjective optimization techniques are required to be able to deal with the problem difficulty. This thesis proposes an efficient multiobjective optimization algorithm named MOAIS, which is based on artificial immune systems. A new unified architecture is developed in the proposed algorithm by integrating both clonal selection principle and immune network theory. A sensitivity analysis is performed to investigate how the parameters defined in the algorithm affect the performance of the algorithm. In addition, comparative studies are made to demonstrate the performance of the proposed algorithm with respect to other advanced multiobjective optimization algorithms. Results show that the proposed algorithm is highly competitive with other state-of-the-art multiobjective algorithms in finding a set of well-converged and well-distributed Pareto-optimal solutions on a number of difficult multi objective test problems. Based on the new MOAIS, a constrained MOAIS is proposed to deal with constrained multi objective optimization problems. An efficient constraint-handling technique is proposed by extending a single-objective constraint-handling technique to multi objective optimization procedure. Comparative studies are performed quantitatively to assess the performance of the constrained MOAIS. Simulation results demonstrate that the constrained MOAIS is highly competitive with other state-of-the-art constrained multiobjective algorithms on a number of complex constrained multi objective optimization test problems. The constrained MOAIS is applied to solving a complex multiobjective cam shape optimization problem for a revolutionary cam drive engine. The superiority and efficiency of the constrained MOAIS are demonstrated through the engineering application.