The process of transitioning from the current once-through nuclear fuel cycle to a hypothetical closed fuel cycle necessarily introduces a much greater degree of supply feedback and complexity. When considering such advanced technologies, it is necessary to consider when and how fuel cycle facilities can be deployed in order to avoid resource conflicts while maximizing certain stakeholder values. A multiobjective optimization capability was developed around the VISION nuclear fuel cycle simulation code to allow for the automated determination of optimum deployment scenarios and objective trade-off surfaces for dynamic fuel cycle transition scenarios. A parallel simulated annealing optimization framework with modular objective function definitions is utilized to maximize computational power and flexibility. Three sample objective functions representing a range of economic and sustainability goals are presented, as well as representative optimization results demonstrating both robust convergence toward a set of optimum deployment configurations and a consistent set of trade-off surfaces.