Forecasting price index is an important problem in financial markets. In the past decades the prediction of stock index has played a vital role in the financial situation of several companies which have stocks in the market. In the past this prediction process was simple and easy for several reasons: the behavior of the stocks was known and not complicated beside the existence of a number of experts in this field. Several techniques are used to predict and model the stock market behavior and try to increase the accuracy of prediction. Neural networks have several characteristics which make them good models to predict the complex behavior of stock index and increase the accuracy of the prediction. Combining neural networks with evolutionary computational methods like Genetic Algorithms and Simulated Annealing can give better results in learning neural networks specially for problem of forecasting stock index.