In midterm planning, the objective of an electricity retailer is to manage a portfolio of different contracts and determine the selling price offered to its clients. Assuming that, within an electricity market framework, rival retailers compete for prices in order to achieve the largest possible number of clients. In addition, the reasonable criterion for the retailer is to select a solution that attains the minimum selling prices while satisfying the constraints. The proposed framework in this paper is modeled in the form of a multiobjective framework to simultaneously maximize retailers' profit and minimize selling prices to clients. The normal boundary intersection method is implemented to generate Pareto-optimal solutions. The best compromise solution is adopted using a fuzzy decision maker. Roulette wheel mechanism and lattice Monte Carlo simulation are employed for random scenario generation wherein the stochastic optimization problem is converted into its respective deterministic equivalents. Benders decomposition has been employed as a robust algorithm to reach the optimal solution. The efficiency of the presented method is verified by making a comparison of its performance with another multiobjective optimization method through a real case study to show its superiority.