In this paper is presented a new approach of an intelligent model for optimization under uncertainty to determine the best strategy of electricity trading in the short term (referring to A-1 and Adjustment auctions) for distribution companies. This model reproduces all the rules of purchase/sale of energy for a distribution company and the transfer of this cost to the final tariff of the consumers. The optimization process uses genetic algorithm, and seeks to minimize the cost associated with the purchase of energy, penalty for subcontracting and the cost of trade (purchase/sale) energy by the spot price. The optimal trading is obtained considering several load scenarios, obtained by Monte Carlo simulation, for a period of five years of analysis. The decisions of trading are taken in the first two years in that period. The evaluation of the model results is done by means of a combination between the expected value of the distribution of costs and the CVaR (Conditional Value at Risk), for the different load scenarios. The model also uses the PLD-robust, which seeks to minimize the exposure of the distribution company in the spot price. To illustrate the results of the proposed model, a study case based on realistic data is presented. The results obtained are compared to the results obtained with the trading of energy without using the optimization model presented in this paper. That comparison is done to verify how much the proposed method can be better than the solutions based on intuitive analysis. In addition, further analysis is performed by considering two mechanisms of compensation of the surpluses and deficits of contracts, named MCSD4% and MCSD-Expost, established by ANEEL to reduce the risks associated to the energy trading to the distribution companies.