A new technique is presented for the design of evolutionary predictive controllers in which the optimum parameters of a predictive controller are computed at each time step. Based on a stochastic search technique using Genetic Algorithms, the technique leads to the design of predictive controllers whose closed-loop performance subject to control constraints is significantly improved over existing predictive control techniques. Examples of both unconstrained and constrained control are presented.