  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 two 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 different ways to select parents and bit-wise mutation optimizes the
OneJumpZeroJump benchmark with jump size~$2 \le k \le n/4$ in time $O(N n^k)$.
When using fast mutation, a recently proposed heavy-tailed mutation operator,
this guarantee improves by a factor of $k^{\Omega(k)}$. 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.
