Dynamic multiobjective optimization deals with simultaneous optimization of multiple conflicting objectives that change over time. Several response strategies for dynamic optimization have been proposed, which do not work well for all types of environmental changes. In this article, we propose a new dynamic multiobjective evolutionary algorithm based on objective space decomposition, in which the maxi-min fitness function is adopted for selection and a self-adaptive response strategy integrating a number of different response strategies is designed to handle unknown environmental changes. The self-adaptive response strategy can adaptively select one of the strategies according to their contributions to the tracking performance in the previous environments. Experimental results indicate that the proposed algorithm is competitive and promising for solving different DMOPs in the presence of unknown environmental changes. Meanwhile, the proposed algorithm is applied to solve the parameter tuning problem of a proportional integral derivative (PID) controller of a dynamic system, obtaining better control effect.