Most industrial active magnetic bearings (AMBs) are controlled by proportional-integral-derivative (PID) controllers. Usually, a time-consuming iterative manual tuning procedure is required to design these PID controllers. In this contribution, we introduce the strategy, algorithms, and results from an AMB controller design, including optimization using a multiobjective genetic algorithm. We focus on the combination of frequency-and time-domain-based optimization, a strategy for the evaluation of fitness functions for complex PID-controller design, and a sensitivity-based parameter reduction for optimization. Two AMB system controller designs are considered and satisfactory results are obtained using the suggested optimization strategy. For validation purposes, the optimized controller design is experimentally implemented for the first AMB system, which contains a flexible test rotor supported by two AMBs. The maximal rotational speed of 15 000 r/min is achieved for the test rotor. A comparison between simulated and experimental results is presented.