  The verification and validation of automated and autonomous driving systems
impose a major challenge, especially the identification of suitable test
scenarios. This work presents a methodology that adopts metaheuristic search to
optimize scenarios. For this, a suitable search space and a suitable fitness
function needs to be created. Starting from abstract descriptions of the
system's functionality and use cases, parameterized scenarios are derived. The
parameters span a search space, in which the suitable scenarios need to be
found. Guided by a fitness function, search-based techniques are used to
identify those scenarios, in which the system shows its worst behavior. If the
derivation of the fitness function is done correctly, an argumentation basis
about test completeness and system quality may be achieved. Further, test goal
oriented testing with automated test oracles is enabled.
