Experience with modeling fuel cycle options reveals that the large amount of generated data makes it difficult to understand trade-offs among fuel cycle policies. This paper shows that numerical optimization can be used to better identify impacts of fuel cycle policies and condense the generated data against a few significant criteria. The once-through cycle is considered the baseline case, while advanced technologies with fuel recycling characterize the alternative fuel cycle options available in the future. The options include, among others, recycling the fissile materials from spent light water reactor fuel in fast reactors (FRs) as well as deployment of innovative recycling reactor technologies, such as the 235U initiated FRs. Additionally, a first-of-a-kind optimization scheme for the nuclear fuel cycle analysis is described. Optimization metrics of interest to different stakeholders in the fuel cycle (economics, fuel resource utilization, high-level waste, transuranic materials/proliferation management, and environmental impact) are utilized for two different optimization techniques: a linear one and a stochastic one. Stakeholder elicitation provided sets of relative weights for the identified metrics appropriate to each stakeholder group, which were then used to demonstrate feasibility of arrival at optimum fuel cycle configurations for recycling technologies. The stochastic optimization tool, based on a genetic algorithm, was used to identify noninferior solutions according to Pareto’s dominance approach to optimization. The main trade-off for fuel cycle optimization was found to be between emphasizing economics versus most of the other identified metrics.