This work addresses decision-making for robot systems that can self-replicate. With the advent of 3D printing technology, the development of self-replicating robot systems is more feasible to implement than it was previously. This opens the door to various opportunities in this area of robotics. A major benefit of having robots that are able to make more robots is that the survivability of the multi-robot system increases dramatically. A single surviving robot that has the necessary capabilities to self-replicate could prospectively repopulate an entire ‘colony’ of robots, given sufficient resources and time. This gives robots an opportunity to take more risks in trying to accomplish an objective in missions where robots must be used instead of humans due to distance, environmental, safety and other concerns. Autonomy is key to maximizing the efficacy of this functionality (or allowing this functionality in a communication limited/denied environment) for this type of robotic system. A challenge of analyzing self-replicating robot systems, and the decision-making algorithms for those systems, is that there isn’t currently a standard means to simulate these systems. Thus, for the purpose of this work, a simulation system was developed to do just this. Experiments were conducted using this simulation system and the results are presented. In this dissertation, the configuration and decision-making of self-replicating 3D printed robot systems are analyzed. First, an introduction to the concepts and topics is provided. Second, relevant background information is reviewed. Third, a simulation, used to model self-replicating robot systems to perform the experiments in later chapters, is detailed. Then, experiments are conducted utilizing this simulation model. These include the analysis of the impact of replication categories on system efficacy, the analysis of the comparative performance of multiple decision-making algorithms, and cybersecurity threats for self-replicating robot systems. For each, data is presented and analyzed, and conclusions are drawn. Finally, this dissertation concludes with a summary of the results presented throughout the document and a discussion of the broader findings from the experiments.