Evolutionary algorithms have often been applied to evolve controllers for robot locomotion. In an attempt to imitate biological systems, most previous approaches have utilized neural networks and central pattern generators to construct the controllers. In contrast to the conventional approach, control points for Hermite splines are evolved, rather than neuron weightings. A spline based control system has the advantages that it is simple to implement, requires little processing power, and the complexity of the controller can easily be altered. Initially limiting the splines control point parameters allows for faster evolution of the initial gait, and by progressively adding extra parameters the initial gait can be refined to produce an optimized final gait. This provides the robot designer with an early approximate gait for the robot, in less time than required to evolve a full control sequence by other means.