From among the many techniques currently available to solve multi-objetive optimization problems (MOPs), an alternative is the use of multi-objective artificial immune systems (MOAISs). This sort of metaheuristic emulates immune processes using computational resources, with the aim of solving MOPs. MOAISs have mechanisms such as the clonal selection principle as well as positive and negative selection, that make them powerful search tools. In recent years, there have been proposals of MOAISs that adopt selection schemes that are more appropriate to deal with many-objective problems (i.e., problems having more than 3 objectives), from which decomposition has been a popular choice. We propose here a new MOAIs called “Multi-objective Artificial Immune System based on Decomposition” (MOAISDX). The performance of our proposed approach is compared with respect to that of NSGA-II and MOEA/D, as well as with respect to four recent MOAISs. The results obtained from this comparative study show that MOAISDX outperforms NSGA-II and obtains results similar to those of MOEA/D in most of the adopted test instances. Furthermore, MOAISDX has better performance than that of the other MOAISs compared, particularly as we increase the number of objectives.