This article introduces a novel constraining approach for structural optimization, which aims to support the conceptual engineer during the early embodiment phase for structural lightweight design. It reduces the time spent on structural engineering studies by enabling optimization algorithms to detect geometric intersections by analyzing the mesh information. This article reviews approaches from the literature focusing on CAD-environments, sampling methods, data analytics and optimization techniques for design and sizing optimization with FE-models. The evaluated approaches are integrated into a Python-based optimization environment. Accordingly, the introduced methodology enables the environment to handle geometric infeasible designs. The presentation of the first results focuses on the feasibility of structural assemblies and the results demonstrate the viability of the NSGA-II for optimization tasks. The example considers the design of a generic b-pillar structure under crashsafety requirements. Using this approach, the NSGA-II algorithm avoids geometric infeasible areas and comparably increases structural performance.