In addition to the need for satisfying several objectives, many real-world problems are also dynamic and require the optimization algorithm to continuously track the time-varying Pareto optimal set over time. This paper proposes a memory enhanced dynamic multi-objective evolutionary algorithm based on decomposition (denoted by dMOEAD-M). Specifically, the dMOEAD-M decomposes a dynamic multi-objective optimization problem into a number of dynamic scalar optimization subproblems and optimizes them simultaneously. An improved environment detection operator is presented. Also, a subproblem-based bunchy memory scheme, which allows evolutionary algorithm to store good solutions from old environments and reuse them as necessary, is designed to respond to the environment change. Simulation results on eight benchmark problems show that the proposed dMOEAD-M not only runs at a faster speed, more memory capabilities, and a better robustness, but is also able to find a much better spread of solutions and converge better near the changing Pareto optimal front, compared with three other memory enhanced dynamic evolutionary multi-objective optimization algorithms.