An autonomous spacecraft rendezvousing with a set of targets sequentially under constraints such as timeliness, priority and Δv cost, can be reducible to a multiobjective wandering salesman problem (WSP) with dynamically moving vertices representing the rendezvous targets. Optimal tours are seen to minimize both the time taken to visit all targets and the total Δv used. Evolutionary multiobjective optimization (EMOO) is well suited to this type of problem, handling mixed discrete and real-valued parameters in complicated environments. This paper presents Orbgnosis, a satellite targeting and trajectory optimization tool that employs the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Baseline studies consider algorithm performance on LEO constellations in several configurations. Optimal tours among formations of up to six satellites are demonstrated and a wide range of Pareto-optimal flight profiles are generated. Results from these simulations are used to predict constellation availability under several types of maintenance strategies. The generated tours ranged from very fast direct intercepts to extremely low Δv, efficient phasing maneuvers, with the ability to guide convergence to the type of tour desired.