There is a growing need for efficient and effective methods of heat dissipation. One driving force for this need is computer processors. As the processor grows faster and more powerful, it requires more electricity to perform tasks which results in high amounts of heat generated. Designing materials with high thermal-conductivity can enable heat dissipation to allow faster and more powerful computers. Creating such materials is often a trial-and-error process by which several material composites are tested for desirable thermal conductivity. In this research, we employed the use of a genetic algorithm, which mimics the process of evolution through natural selection, as an alternative to exhaustive trial-and-error approaches to help design a graphene based template material with high thermal conductivity. The algorithm creates a population of randomly generated configurations, then uses an open source physics (molecular dynamics) simulator, LAMMPS, linked with High-Performance Computing to run a molecular dynamics simulation for each composite to derive a fitness, or score for the material. The highest scoring materials undergo crossover to create offspring for the next generation. Over time, these algorithms have the potential to find a composite with desirable conductivity through this pseudo-evolution process.