Genetic Programming (GP) combined with Decision Trees is used to evolve the structure and weights for Artificial Neural Networks (ANN). The learning rule of the decision tree is defined as a function of global information using a divide-and-conquer strategy. Learning rules with lower fitness values are replaced by new ones generated by GP techniques. The reciprocal connection between decision tree and GP emerges from the coordination of learning rules. Since there is no constraint on initial network, a more suitable network is found for a given task. Fitness values are improved using a Hybrid GP technique combining GP and Back Propagation. The proposed method is applied to medical diagnosis and results demonstrate that effective learning rules evolve.