This work presents a methodology that gives more accuracy to the handling of uncertainty in optimization process of production strategies. In this methodology, pessimistic, intermediate and optimistic realizations for the geological model and the economic environment can be considered. Differently from other methodologies, this consideration is made simultaneously during the optimization process. The process is guided by the expected monetary value (EMV) of project. It gives a greater adaptability to the optimized production strategy. An algorithm that carries out the EMV of a production strategy for each step of the optimization was developed. This algorithm performs the interconnection between flow simulation and economic analyses, necessary to calculate the EMV of a production strategy. It is called at every time where the optimization process requires the objective-function evaluation. In this work the methodology proposed by Nogueira and Schiozer, 2009 for production strategies optimization based on genetic algorithms is utilized. The performance of the proposed methodology is compared with the performance of a conventional methodology of production optimization under uncertainty. A case with three geological models and three economic scenarios is studied. The optimized strategy presents more adaptability to the possible variations, showing a good performance in all adopted contexts. Although the proposed methodology represents a more accurate process than the conventional methodology, there is a decrease in the number of flow simulations required to optimize the production strategy. The proposed methodology is shown as a viable alternative for handling of uncertainties for optimization process of production strategies. Copyright 2009, Society of Petroleum Engineers.6 35193528Newendorp, P.D., (1975) Decision Analysis for Petroleum Exploration, , Penn Well Publishing Co, Tulsa, OklahomaNogueira, P.B., (2008) Methodology of Production Strategy Optimization Based on Genetic Algorithms, , Dissertation Master Degree, Portuguese State University of Campinas, BrazilNogueira, P.B., Schiozer, D.J., An Efficient Methodology of Production Strategy Optimization Based on Genetic Algorithms SPESchiozer, D. J., Ligero, E. L, Suslick, S. B., Costa, A. P. A. e Santos, J. A. M.: Use of Representative Models in the Integration of Risk Analysis and Production Strategy Definition, Journal of Petroleum Science and Engineering, pag. 131-141, numbers. 1-2, 44, October, 2004