A-check maintenance scheduling is a complex and critical undertaking for airlines requiring an efficient allocation of resources. Current state-of-the-art focuses primarily on long-term A-check planning, typically targeting a longer scheduling horizon while foregoing individual task planning. This paper introduces a novel integrated approach for A-check scheduling at a seasonal level for an airline fleet, which accounts for both repetitive and one-off maintenance tasks. The A-check maintenance scheduling problem is formulated as a mixed-integer linear program (MILP), which optimises for minimal interval waste and timely initiation of one-off activities. Furthermore, we explore the scalability and flexibility of this problem by proposing three distinct distributed architectures. Subsets of maintenance tasks are scheduled by individual components, guided by a genetic algorithm (GA) acting as a global optimiser, with each architecture managing shared resources differently. We demonstrate our method with a case study from a major European airline using recent data of a fleet of wide-bodied passenger aircraft. While our MILP baseline produces comparable results to real-world schedules within minutes, the distributed architectures, despite their potential for scalability, generally underperform compared to the central planner. We analyse the degradation of solution quality across these distributed architectures, providing insights into their design limitations and the inherent indivisibility of the problem. We propose that our central MILP-based scheduler can be used by airlines as a decision-support tool for A-check task planning at the seasonal level.