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Multi-agent Credit Assignment (MCA) problem is considered as one of the critical challenges in developing Multi-Agent Reinforcement Learning (MARL). The MCA problem addressed how to distribute global reward, which is received by the Multi-Agent System (MAS) owing to an interaction with the environment among the agents. In this paper, a two-step method is proposed to solve the MCA using the bankruptcy and Evolutionary Games (EG). The first phase turns the MCA problem into a bankruptcy game through introducing the constraint of Maximum Performance Power (MPP). In this game, the agents act as players through a mixed strategy. The game’s outcome is the each agent’s share of the global reward. An instance of credit assignment is extracted by determining each agent’s share from the global reward. Using the mixed strategy leads to generate a lot of credit assignment instances. Therefore, finding …