AbstractSimulation-based optimization combines simulation experiments used to evaluate the objective and/or constraint functions with an optimization algorithm. Compared with classical optimization, simulation based optimization brings its specific problems and restrictions. These are discussed in the paper. Evaluation of the objective function is based on time consuming, typically repeated simulation experiments. So we believe that the main objective in selecting the optimization algorithm is minimization of the number of objective function evaluations. In this paper we concentrate on integer optimization that is typical in simulation context. Local search algorithms that try to minimize the number of objective function evaluations are described. Examples with both analytical and simulationbased objective functions are used to demonstrate the performance of the algorithms.