The navigation parameters used in existing geomagnetic navigation research are most of original geomagnetic parameters-based. The selection of geomagnetic parameters may affect the efficiency of navigation, and there is man-man error in the process of selecting navigation parameters. For the above-mentioned reasons, a search navigation method is proposed based on the geomagnetic feature extraction. The extracted geomagnetic features able to comprehensively describe the magnetic field information of the location are taken as new navigation parameters by the principal component analysis method. In combination with the existing evolutionary search strategies and gradient descent methods, the extracted geomagnetic features are fully utilized for taking as new navigation parameters to guide the carrier to continuously approach the target and achieve navigation objectives. The experimental results show that the traditional search navigation method based on the original geomagnetic parameters has an iteration step of 647 for the objective function, while the search navigation method based on extracting principal component features has an iteration step of 564 for the objective function. However, the evolutionary gradient navigation method based on principal component features proposed in this paper has an iteration step of 238, significantly reducing the iteration step number, improving navigation efficiency and the consistency of sub objective function convergence. The original magnetic field information is utilized further in autonomous remote navigation, and there are good prospects.