The purpose of this research is to provide an intelligent model for prediction of golden points on stock price chart as a decision support system. For conduction of this research, the data of the automotive and parts manufacturing industry during 2001 through to 2016 were used. First, the obtained results from application of different forecasting models based on data mining were compared with each other. Next, the research variables were optimized by genetic algorithm and remodeling took place. The results indicated that the golden points could be predicted with reasonable accuracy and optimization did not enhanced accuracy in all these models, yet it significantly reduced gross error.