In the classic approach for optimization problems modelling well defined parameters are assumed. However, in real life problems we find ourself very often in a situation where parameters are not defined precisely. This may have many sources like inaccurate measurement, inability to establishing precise values, randomness, inconsistent information or subjectivity.In this paper we propose a sampling method for solving optimization problems with uncertain parameters modeled by random variables. Moreover, by applying confidence intervals theory, the execution time has been significantly reduced. We will also show an application of the method for the flowshop problem with deadlines and parameters modeled by random variables with the normal distribution.