The economic decision on investment and evaluation of projects are affected by economic and technical uncertainties and by management flexibilities inserted on projects. These management flexibilities give the manager freedom to take decisions, such as to invest, to expand, to temporarily stop or to abandon a Project. These flexibilities have value and only can be evaluated thhrogh real option theory. The use of real options considers uncertainties and management flexibilities with the objective of maximizing the value of the investment opportunity. To determine the value of the real option, models of binomials tree, finite differences or Monte Carlo simulation techniques are normally used. However, the traditional methods of binomials tree and finite differences are impracticable in the evaluation of options with more than three uncertainties, while the Monte Carlo simulation presents a high computational cost due to the iterative process of the stochastic simulation in sampling each variable. The objective of this work is to investigate a computational methodology that can be used to determine the value of real option under diverse uncertainties, both of technical and market types. Therefore, this work investigates methods that can reduce computational time and thus create means for taking decisions. For this purpose, the union of several techniques is proposed: fuzzy numbers to represent some types of uncertainties of which an adequate stochastic process is unknown, stochastic process to represent other uncertainties and the Monte Carlo simulation to obtain a good approximation of the value of real options. Moreover, a genetic algorithm, together with Monte Carlo simulation, is used to approximate an optimum decision rule and to determine the value the real option when several investment options are available in a project. The rule helps decide whether to make an immediate investment in an option or to wait for better conditions; this is dependent on the state of the uncertainties considerated. The proposed model was evaluated in problems of options of expansion and of investment in information, applied in the area of oil exploration and production. Results obtained were similar to those achieved by conventional techniques, with a substantial reduction in computational time. The main contribution of this work is the conception of a new methodology for the determination of the value of real options with technical and market uncertainties. This methodology has shown to be advantages in relation to conventional methods. Results show that the use of fuzzy numbers to represent uncertainties of which the stochastic process that shapes them is unknown reduces the computational time significantly. Moreover, the methodology demonstrates that the genetic algorithm is an adequate technique for approximating a decision rule when many investment options are considered.