Maintenance actions constitute critical tasks that ensure the availability of industrial systems and improve their operating safety. However, maintenance faces numerous challenges and is no longer limited to guarantying availability. It has become a strategic concern and abides by imposing quality, safety, and cost requirements. Finding optimal grouping strategies of maintenance activities is an NP-hard problem that is well studied in the literature, and for which various economic models and optimization approaches are proposed. While most models found in literature use heuristics, such as evolutionary algorithms, to locate cost-reducing grouping strategies, context-specific constraints that could arise within each system are not taken into consideration. For large complex systems, heuristic approaches cannot guarantee the convergence to a feasible solution. Therefore, we propose a new scalable and adaptive optimization algorithm based on a clustering approach to group maintenance activities in multi-component complex systems. The proposed Constrained Clustering-based approach takes into consideration domain-dependant constraints and provides grouping strategies in negligible times.