Advancement in testing and verification methodologies is one of the key requirements for the commercialization and standardization of autonomous driving. Even though great progress has been made, the main challenges encountered during testing of autonomous vehicles, e.g., high number of test scenarios, huge parameter space and long simulation runs, still remain. In order to reduce current testing efforts, we propose an innovative method based on surrogate models in combination with stochastic optimization. The approach presents an iterative zooming-in algorithm aiming to minimize a given cost function and to identify faulty behavior regions within the parameter space. The surrogate model is updated in each iteration and is further used for intensive evaluation tasks, such as exploration and optimization.