Infrastructure inspection using unmanned aerial drones has a great potential to support complex inspection tasks especially where inspection task can be dangerous, dull or dirty. The increased number of systems in this type of inspection process makes it a very complex systems-of-systems (SoS) which is hard to assess. As a result, it becomes very difficult to satisfy all stakeholder needs and requirements. Therefore, an assessment system is required that can efficiently assess the meta-architecture of drone based inspection system. This paper presents a method to generate and evaluate systems of systems (SoS) architecture model for aerial inspection with drones. Where, a meta-architecture containing system component and a system to system interface is presented. To map the desired SoS attributes from stakeholders, different characteristics of the architecture capabilities are evaluated using some linguistic terms called key performance attributes (KPA). KPAs are combined in a Fuzzy Inference System (FIS) to evaluate an overall fitness value that is optimized using a Genetic Algorithm (GA) for the SoS within the meta-architecture. The integrated evaluation method presented in this paper utilizes the SoS explorer to evaluate the SoS meta-architecture using synthetic parameter values.