The model of this thesis simulates a simple artificial eco-system in which evolving and learning agents try to survive by consuming balls of energy and surviving attacks by other agents. The study finds that the model indeed manages to evolve surviving, and in some cases very aggressive, agents. The thesis presents similar conclusions to that of the study of Polyworld by Yaeger [16]; that an evolving population only facilitates a need for complexity set by the world it evolves in and stagnates when the population has reached this level of complexity. If the populations are to evolve further, the world it lives in must first demand a higher level of complexity. Various problems with simulating artificial life are also discussed along with the more specific obstacles of simulating artificial life in Breve and NEST integrated. The physical world of the model is built in the Breve simulation environment and the neural networks are simulated in NEST through integrate-and-fire neurons and spike-timing dependent plasticity synapses.