The design and evolution of today's embedded systems face an increasing complexity both in the applications and in the platforms that support them. The use of complex platforms means that the engineers need to make non-trivial decisions during the design phase including optimized allocation, binding and scheduling. This situation creates a need for better ways to manage project complexity, and Model Driven Engineering is a possible solution. In this work, we propose an extension of a Model Driven Engineering methodology (i.e. HIPAO) to help manage complexity, which includes an automated Design Space Exploration phase that uses Genetic Algorithms to find the best hardware/software partitioning for a given application. We apply the HIPAO methodology to a case study of an autonomous industrial inspection system.