With the advent of the new model for the electricity sector in 2004, market agents were allowed to sell energy in the free market. Considering the nature of these operations and the influence of meteorological variables on the formation and volatility of prices, energy trading decisions are taken under conditions of uncertainty, leading agents to seek contracting strategies to maximize the return on assets or mitigation of the risks involved. In the Brazilian electricity sector, market risk management is mainly accomplished through forward contracts, in order to reduce the adverse impacts of PLD fluctuation. In this context, the objectives of this study are to evaluate the applicability of two optimization models under uncertainty, single-stage and two-stage stochastic, in the decision making of a trading company and to compare the decisions recommended by the models. These models used a preference function that allows representing the variation of the risk aversion level considering different preference groups, having its parameters determined by the Analytic Hierarchical Process. For the construction of the forward curves of the two-stage stochastic model, the observed market price and the 2,000 PLD series of the ONS official forecast were weighted. The results evidenced the effectiveness in risk mitigation for the evaluated products. Furthermore, due to the reduction in the cost of regret from the two-stage optimization problem modeling, this model presented more cost-effective solutions when compared to the single-stage model.