The use of multiple shooting has become the standard for the numerical solution of optimal control problems. We investigate how multiple shooting affects the convergence properties of Newton-type methods. For the first time, we
conduct a systematic comparison of several multiple shooting strategies on a set of 40 optimal control problems. In addition, we consider differences between interior-point and sequential quadratic programming methods, accounting
for both Quasi-Newton approximations and exact Hessians. Based on these observations, we propose an adaptive multiple shooting algorithm that reduces the number of iterations by about 27% on average across all problems and
by more than 50% for selected problems compared with naïve multiple shooting approaches.