The emerging field of Evolutionary Computation (EC), inspired by neo-Darwinian principles (e.g., natural selection, mutation, etc.), offers developmental psychologists a wide array of mathematical tools for simulating ontogenetic processes. In this brief review, I begin by highlighting three of the approaches that EC researchers employ (Artificial Life, evolutionary robotics, and comparative stochastic optimization). I then focus on the advantages of using comparative stochastic optimization as a method for studying development. As a concrete example, I illustrate the design and implementation of an EC model that simulates the development of reaching in young infants. (Matthew Schlesinger)