Gait phase (GP) estimation is a critical component in control of exoskeletons and prostheses, enabling seamless user interaction in various controllers. In recent years, methods based on machine learning and sensor fusion have offered advances in GP estimation, but their high computational costs and reliance on training and numerous sensors present practical challenges. Estimation methods using phase variables, such as phase-portrait-based methods, can circumvent these drawbacks. However, their lower accuracy has limited their application. To address this limitation, we introduce a novel human-in-the-loop (HIL) optimization approach for improving the accuracy of GP estimation in phase-portrait-based methods. The approach is based on geometric manipulation of the phase portraits with linear transformations, which are adapted online by employing Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The performance of this adaptive method (termed AM) is compared against using a constant transformation (CM) at different walking speeds in level-ground and uphill walking. The results demonstrate the superior performance of AM in all tested conditions in terms of accuracy and linearity, with an average RMS error of 1.97 ± 0.20%. Convergence times for one round of optimization on a low-end single-board computer were less than 11 s on average. This study confirms the potential of leveraging HIL optimization for enhancing the performance of phase-portrait-based methods to reach accuracy levels comparable to more complex state-of-the-art methods.