Wearable robotic exoskeletons are frequently explored for their efficacy in physical rehabilitation and for assistance in daily activities in people with motor disorders, yet relatively few have convincing evidence for use. The concept of human-in-the-loop optimization has been used to identify ideal exoskeleton assistive torques based on measured individual performance metrics, whereas few studies report optimizing several performance metrics simultaneously. In this study, we propose a multi-objective human-in-the-loop optimization that adjusts exoskeleton assistive profiles to improve several gait quality measures, specifically foot segment kinematics and step length symmetry. A preliminary proof-of-concept evaluation was conducted with five non-disabled participants, where a weighted shoe was used to simulate the gait deviations associated with dropfoot and excessive inversion. The gait quality metrics improved more for each new generation. With optimal assistive solutions, foot segment kinematics improved (10 mm higher foot clearance height and a 7∘ increase in inclination angle) and step length became more symmetric (asymmetry reduced from 9% to 2%), compared to simulated impairment. Within this set of solutions, each represents a unique balance between the two objectives, wherein a solution that prioritized normalizing step length symmetry could slightly sacrifice normalizing foot segment kinematics, and vice versa. By taking advantage of the trade-off relationship between the two objectives, more flexible, individualized, and situation-dependent assistance can be obtained. These results demonstrate the method's usefulness in determining subject-specific exoskeleton control parameters that improve several measures of gait. Future applications include refining this protocol for actual dropfoot, offering personalized assistance to restore their functional mobility and reduce compensatory movements, and validating its long-term effects.