In this paper a multiobjective differential evolution algorithm called Generalized Differential Evolution is extended to solve dynamic multiobjective optimization problems (DMOPs). The proposed algorithm combines the ideas of the generalized differential evolution and the artificial immune system to create a hybrid algorithm which uses the advantages of both approaches. When a change is detected in the environment by a solution reevaluation mechanism, an immune response is activated. The approach is compared against other dynamic multiobjective algorithms in a recently proposed benchmark. Experimental results show that the proposed approach can track the environmental change and has a very competitive performance solving different types of DMOPs.