Dynamic pricing is a kind of pricing strategy in which the price of products varies based on present demand value. So far, several research works have been reported for using neural network for pricing, such as predicting demand and modeling the customer's choices. However, less work has been performed on using them for optimizing pricing policies. In this project, we try to explain the way of combining neural network and evolutionary algorithms to optimize pricing policies. We create a neural network on the basis of demand model and benefit from evolutionary algorithms for optimizing the resulted model. This has got two privileges: First, necessary flexibilities are created by using neural network to model different demand scenarios that is occurred with different products and services, and second, using evolutionary algorithms provides us with the ability of solving complicated models. Wavelet neural network has been used and the resulted pricing policy has been compared with other demand models that are widely used. The results show that the suggested model match up well under different scenarios and presents a better pricing policy than other suggested models.