Designing soft robots is difficult, time-consuming, and non-intuitive. Instead of requiring humans to engineer robots, this research uses genetic algorithms to evolve designs for robots that move when vibrated. Generative encodings are used to represent designs and are modified during the evolutionary process. A generative encoding is a set of rules that describe how to construct a 3D object. If the rules are applied over and over, it will create a larger and more complex robot. Current methods of evolving generative encodings fix a number of times to apply the rules a priori. This restricts the usefulness of the resulting encoding to one size. Instead, this research aims to create scalable solutions, with generative encodings capable of producing fit robots of various sizes. This is done by implementing the notion of a pareto frontier into the genetic algorithm. Each design produced is assigned to a category (small, medium, large, etc) and is judged on how well it can create robots that move when vibrated in each category. One design is deemed better than another if it can dominate across all categories. The resulting generative encodings should be able to produce soft robots of various sizes that can move when vibrated.