Dynamic multi-objective optimization problems (DMOPs) have been rapidly attracting the interest of the research community. Although static multi-objective evolutionary algorithms have been adapted for solving the DMOPs in the literature, some of those extensions may have high running time and may be inefficient for the given set of test cases. In this paper, we present a new hybrid strategy by integrating the memory concept with the NSGA-II algorithm, called the MNSGA-II algorithm. The proposed algorithm utilizes an explicit memory to store a number of non-dominated solutions using a new memory updating technique. The stored solutions are reused in later stages to reinitialize part of the population when an environment change occurs. The performance of the MNSGA-II algorithm is validated using three test functions from a framework proposed in a recent study. The results show that performance of the MNSGA-II algorithm is competitive with the other state-of-the-art algorithms in terms of tracking the true Pareto front and maintaining the diversity.