The performance achieved with traditional model-based control system design approaches typically relies heavily on accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task; and the usually necessary practical approximations often renders the automation system to operate in a nonoptimal condition. This problem can be greatly aggravated in the case of a multiaxis magnetically levitated (maglev) nanopositioning system where the fully floating behavior and multiaxis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under nonoptimal conditions. These motion data essentially contain rich information; therefore, the possibility exists to develop an intelligent automation system to learn from these motion data, and to drive the system to operate toward optimality in a data-driven manner. Along this line then, this article proposes a data-driven model-free controller optimization approach that learns from the past nonoptimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multiobjective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multiobjective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. In this article, experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available.