Today, a lot of decisions affecting sustainability in AEC-industrial projects are made in the conceptual phase. Only a few concepts and performance criteria’s are initially analysed due to the lack of software integration between disciplines. In this study a parametric modelling platform is used to develop a method to integrate and analyse different performance criteria’s in the early design stage, in an iterative form-finding approach with the help of a Genetic Algorithm. The structural performance is in focus with a two-level approach, which initially optimizes the load effects on the structure that in-turn are verified and designed according to Eurocode 3. The Genetic Algorithm is powered by updates in the structures’ geometry and are evaluated accordingly to the user-defined performance criteria’s. It starts by an iterative random population of different geometries and then performs cross-mutations between the superior solutions in regard to the design criteria. The method is applied on a case project defined by a simple symmetric double-curved roof structure made of steel. The governing criterion is displacements given by the glazing-panel fabricator. Utilization of bars, mass and eigenmodes are secondary effects that arise from assigning the Genetic Algorithm specific design criteria. The result shows 80 % reduction of the displacements compared to the architectural proposal. It also indicates slight improvements compared to another type of form-finding technique, inspired by Dynamic Relaxation. The method has proven to be successful since the parametric platform increases the ability to scale the structure according to the designer’s preference. The results show that a large amount of different concepts can be analysed by a multi-criteria analysis in a very short time. This enables the designer to spend more time in the early design of a project, rather than overcoming time-consuming obstacles in the later detailed design stage.