This paper presents a Multiagent Systems based design approach for designing a self-replicating robotic manufacturing factory in space. Self-replicating systems are complex and require the coordination of many tasks which are difficult to control. This paper presents an innovative concept using Multiagent Systems to design a robotic factory for space exploration. Specifically presented is an approach for coordinating a conceptual model of a self-replicating system. The arrival of a set of agents on an unknown planet is simulated, whereby these simple agents will expand into a self-replicating factory using the regolith gathered from the surface of the planet. NASA is currently investing in space exploration missions that consider using the resources on the surface of other planets, asteroids or satellites. The challenge of the project is in the implementation of a learning algorithm that allows a large number of different agents to complete simultaneous tasks in order to maximize productivity. The simulation in this work is able to present the coordination of the agents during the construction of the factory as the pa-rameters of the learning algorithm are changed. System performance is measured with a pre-programmed method, using local and difference rewards. The results show the advantage of using a learning algorithm to better build the robotic factory.