The need for on-demand satellite acquisitions for Earth observation is rapidly increasing, along with the availability of public and commercial satellite constellations capable of fulfilling such requests. Scheduling acquisitions optimally remains a computationally challenging task, especially in complex scenarios with multiple satellites, constraints, and high number of requests. Today, heuristic algorithms are commonly used to find sub-optimal solutions that balance computational time and acquisition value, for example in terms of economic return. The potential role of quantum optimization in this domain is still an open question. While recent demonstrations have explored quantum approaches, they are limited to small problem instances due to current hardware constraints and the difficulty of simulating large quantum systems with classical hardware. In this work, we present a hybrid quantum-classical optimization approach to address acquisition scheduling problems of any size. At each iteration, a classical heuristic generates a global candidate solution, from which a smaller local sub-problem is extracted and solved using a quantum optimization algorithm. We validate the method on large-scale instances, leveraging D-Wave Advantage to solve the sub-problems with quantum annealing. Initial results show the feasibility of the approach and highlight the capability of quantum annealing to guide the hybrid solver towards better overall acquisition schedules than simulated annealing in a comparable amount of time, in particular as the complexity increases.