Missions involving multiple spacecraft have been in the spotlight during the last years due to the number of scientific and engineering advantages they offer. They are crucial in the context of Earth monitoring, disaster management and data relay missions which provide a way of addressing the increasing demand of the volume of data produced on-board daily. The spacecraft are equipped with complex instruments of many capabilities and diverse constraints which exponentially increase the possible state of the constellation at each moment. Hence, their management has become too complex for human operators to handle. To this end, automated mission planning systems are designed and implemented; the operator defines a goal on a higher level, i.e. submits an imaging request, and the system is responsible for determining the activities to achieve it, taking into account the mission's capabilities and constraints. The main challenge when designing such systems lies in finding methods that reliably produce optimal or near optimal solutions while also satisfying certain scalability and responsiveness system requirements.

In this work, we consider two different target missions and propose a ground-based space mission planning framework to address their planning problems. A coverage planning problem for the Disaster Monitoring Constellation 3 mission from Surrey Satellite Technology Limited is solved; an Earth imaging mission consisting of 3 agile spacecraft, that is expected to image 1 million square km each day. We also address the planning problem of a data relay mission, where Geostationary spacecraft act as relays of data among low Earth orbit spacecraft and ground stations. The flexible LEO spacecraft requests have different priorities and are far more than the GEO spacecraft can accommodate, resulting in an oversubscribed scheduling problem. Both problems are of very high computational complexity for exact methods to be efficient. We explore the potential of a stochastic algorithm that is based on the ants' foraging mechanism, Ant Colony Optimisation. Applying this nature-inspired technique to the two planning problems, we manage to achieve optimisation of their schedules while coordinating the constellation's spacecraft. In order to further investigate the capabilities of the method, we introduce and analyse the stability of a non-linear system that models the long-term ACO dynamics in directed graph environments. The analysis provided insights that have proved helpful in increasing the algorithm's efficiency. The performance of the designed system is validated by comparing our results with a Squeaky Wheel Optimisation approach, a method widely used in recent years in the space mission planning field.