Latin hypercube sampling was used to select design points for simulations in order to create surrogate models for optimization problems. Different surrogate models were tested for their performance on various optimization tasks. Radial basis neural networks showed the best overall performance in exploring the domain space and finding optimal solutions. It is recommended to use an initial sample size equal to 15 times the number of design variables when building a surrogate model using Latin hypercube sampling.