Mass vaccination is essential for pandemic control, but long queues can increase infection risk. We study how to schedule arrivals at a mass vaccination site to minimize a tri-objective function of a) expected number of infections acquired while waiting, b) throughput, and c) overtime. Leveraging multi-modularity results of a related optimization problem, we construct a solution algorithm and compare our results to an equally-distributed, equally-spaced schedule. While we find that the latter sits near the pareto-optimal frontier, it is located away from a sharp elbow in the tradeoff between infections and overtime. Specifically, the elbow-policy achieves approximately 55% fewer expected infections for nearly the same expected overtime.  We also discuss managerial insights around the structure of the optimal schedule and compare it to the well-known “dome-shaped” policies found in other appointment scheduling contexts.