Simulation optimization (SO) problems can be difficult to solve because of the lack of knowledge of the algebraic model equations and the unknown structure of the noise inherent to the simulation. It is important to investigate approaches capable of handling noise in order to achieve optimal solution with efficiency. In recent years, surrogate based methods for SO problems have gained increasing attention from different research communities. In this work, we adapted a one-stage adaptive sampling approach to a Kriging-based optimization framework for simulations with heteroscedastic noise. We compared its performance with another Kriging-based approach using expected improvement as the infill criterion. On the basis of the results of several test problems, each with various noise scenarios, we discussed the benefits and limitations of both algorithms. Finally, we show the application of both algorithms to finding the optimal operation conditions of a continuous pharmaceutical manufacturing simulation model.