Recent exponential growth of investors in stock markets brings the idea to develop a predictive model to forecast the total risk of investment in stock markets. In this paper, an evolutionary approach was proposed to predict the total risk in stock investment based on an S&P 500 database in a time period of 1991-2010 employing a multi-objective genetic programming along with an adaptive regression by mixing algorithm. The reasonable results suggest that the proposed model can be applied to various stock databases to assess the total risk of investment. The proposed model along with stock selection decision support systems can overcome the disadvantages of weighted scoring stock selection.