Artificial Neural networks have found many applications in various fields such as function approximation, time-series prediction, and adaptive control. The performance of a neural network depends on many factors, including the network structure, the selection of activation functions, the learning rate of the training algorithm, and initial synaptic weight values, etc. Genetic algorithms are inspired by Charles Darwin’s theory of natural selection (“survival of the fittest”). They are heuristic search techniques that are based on aspects of natural evolution, such as inheritance, mutation, selection, and crossover. This research utilizes a genetic algorithm to optimize multi-layer feedforward neural network performance and structure. The goal is to minimize both the function of output errors and the number of connections of network. The algorithm is modeled in C++ and tested on several different data sets. Computer simulation results show that the proposed algorithm can successfully determine the appropriate network size for optimal performance. This research also includes studies of the effects of population size, crossover type, probability of bit mutation, and the error scaling factor.