  Space exploration plans are becoming increasingly complex as public agencies
and private companies target deep-space locations, such as cislunar space and
beyond, which require long-duration missions and many supporting systems and
payloads. Optimizing multi-mission exploration campaigns is challenging due to
the large number of required launches as well as their sequencing and
compatibility requirements, making the conventional space logistics
formulations not scalable. To tackle this challenge, this paper proposes an
alternative approach that leverages a two-level hierarchical optimization
algorithm: a genetic algorithm is used to explore the campaign scheduling
solution space, and each of the solutions is then evaluated using a
time-expanded multi-commodity flow mixed-integer linear program. A number of
case studies, focusing on the Artemis lunar exploration program, demonstrate
how the method can be used to analyze potential campaign architectures. The
method enables a potential mission planner to study the sensitivity of a
campaign to program-level parameters such as logistics vehicle availability and
performance, payload launch windows, and in-situ resource utilization
infrastructure efficiency.
