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The 0/1 Multiple Knapsack Problem is an important class of combinatorial optimization problems, and various heuristic and exact methods have been devised to solve it. Genetic Algorithm (GA) shows good performance on solving static optimization problems. However, sometimes lost of diversity makes GA fail adapt to dynamic environments where evaluation function and/or constraints or environmental conditions may change over time. Several approaches have been developed for increasing the diversity of GA into dynamic environments. This paper compares two of these approaches named Random Immigrants Based GA (RIGA) and Memory Based GA (MBGA). Results show that MBGA is more effective than RIGA for The 0/1 Multiple Knapsack Problem in a changing environment.