Very recently, the first mathematical runtime analyses of the multi-objective evolutionary optimizer NSGA-II have been conducted. We continue this line of research with a first runtime analysis of this algorithm on a benchmark problem consisting of multimodal objectives. We prove that if the population size N is at least four times the size of the Pareto front, then the NSGA-II with four standard ways to select parents, bit-wise mutation, and crossover with rate less than one, optimizes the OneJumpZeroJump benchmark with jump size 2≤k≤n/4 in time O(Nnk). When using fast mutation instead of bit-wise mutation this guarantee improves by a factor of kΩ(k). Overall, this work shows that the NSGA-II copes with the local optima of the OneJumpZeroJump problem at least as well as the global SEMO algorithm.