U-Space drone operations are expected to be a driver for further urban development, especially through use cases such as medical and commercial parcel delivery. In particular, package delivery using small drones shows great promise, with e-commerce giants such as Amazon deploying limited-scale drone delivery trials in rural areas. As the technology matures, large-scale operations will take place in constrained urban areas, leading to high airborne traffic densities. It is necessary to develop a robust automated separation management system that actively ensures safe separation of drones both in the air and on the ground. This paper focuses on the Strategic element, more specifically on pre-departure planning. The aim of this is to reduce the chance of conflicts around vertiports, where spatial and environmental constraints make tactical resolutions difficult. This work focuses on two scenarios: a single pad for both takeoffs and landings (in a spatially constrained urban area) and 4 takeoff-landing pad pairs (for a distribution center). Several methods are compared for this takeoff sequencing task, coupled with a conflict detection algorithm: A First-Come First-Served method that applied delay to conflicting flights, a Mixed-Integer Programming approach, a Genetic Algorithm, Particle Swarm Algorithm and Simulated Annealing were used. For a single-pad approach, first-come first-served works best in terms of computation time and total deployment time (or makespan). For the multi-pad approach however, changing the flight sequence through metaheurisitic methods and mixed-integer linear programming show a reduction in total deployment time.