Oil companies make a great effort to maintain profitability and improve efficiency, especially given the uncertainties present in this business. Companies that intend to remain competitive need to plan their operations better and with greater safety. In light of these opportunities and challenges, this thesis proposes a stochastic approach to the refinery operational planning problem. In this sense, a two-stage nonlinear stochastic programming model (NLP) developed. The proposed model is intended to adequately represent nonlinear processes encountered in a refinery, such as chemical transformations and calculations of the properties of the oil derivatives. Due to the high level of complexity of the NLP problem formulated, five solution methods associated with major commercial solvers were evaluated. A methodology for generating scenarios and quality measures for scenarios tree were also defined to properly represent the uncertainties present in this problem. The stochastic approach proposed in the present study was evaluated based on actual data from a Brazilian refinery. The final results of this research should provide advances in the processes of refinery operational planning exploiting the technique of nonlinear programming (NLP) and new solvers available for NLP-type problems. Another objective was to generate contributions in the field of stochastic programming by defining quality measures for scenario trees that allow a better representation of uncertainties and, consequently, better use of the stochastic approach.