This paper presents a model for the computation of costs of the generation of the electrical power in a power system. It is based on the stochastic optimization of mid-term generation scheduling wind integrated power system with respect to the uncertainty on both wind generation and load forecast. The uncertainty of the two above mentioned elements is modeled by scenario-based method. In the previous works, the impact of the generation of wind power on the generation scheduling (GS) problem using stochastic methods has been studied on daily basis. However, this study assesses the effect of the generation of wind power on the GS problem on a yearly manner. The problem of GS is solved under the various constraints of the power system. These constraints embrace the thermal and wind units' constraints, power balance, available wind power, and reserve requirements. A population-based method called teaching-learning-based-optimization (TLBO) is proposed to solve the stochastic generation scheduling problem. TLBO is a robust and effective search algorithm. The most salient advantage of this algorithm is that it does not require the tuning of any kind of controlling parameters. The efficiency of the proposed formulation is studied by employing the three test cases. Numerical results are presented for these case studies.