Routing and scheduling problems with increasingly realistic modelling approaches often entail the consideration of multiple objectives, time constraints, and modelling the system as a multigraph. The latter is required in multiple applications to represent alternative routes with different costs linking the same nodes. The detailed modelling approach increases computational complexity and may also lead to violation of the additivity property of costs. Therefore approximate solution methods become more suitable. This paper focuses on one particular real-world application, the Airport Ground Movement Problem, where both time constraints and parallel arcs are involved. We introduce a novel Memetic Algorithm for Routing in Multigraphs with Time constraints (MARMT) and present a comprehensive study on its different variants; these variants are based on diverse genetic representation methods. We propose a local search operator that provides significant improvements. Our results also show that the best variant of MARMT is consistently producing high quality results in shorter times compared to a state of the art enumerative algorithm. The algorithms are tested on real data. MARMT can be adapted for other applications with minor modifications, such as train operations or electric vehicle routing.