The registration of optical and synthetic aperture radar (SAR) images is crucial for multisource remote sensing image analysis and application. Due to different imaging mechanisms, the repeatable key points between optical and SAR images are scarce. Recent works have mainly focused on improving feature detection and description, but the matching strategies still rely on neighboring search, which only considers the visual appearance of key points while neglecting the spatial relationships. To resolve the issue, this letter proposes a two-stage registration algorithm based on combined features and graph neural networks (GNNs). First, to obtain more repeatable points, a convolutional neural network (CNN) is designed by combining detector-free and detector-based strategies, of which the former generates feature points with a grid-like distribution and the latter detects key points with a distinctive appearance. Second, GNNs are utilized for feature matching. The positional information of feature points is embedded into descriptors, and the information from other feature points is aggregated through an attention-based context aggregation mechanism to enrich feature descriptions. Third, a two-stage registration framework is adopted to raise the registration accuracy. Finally, the experimental results show that the proposed method performs excellently for the registration of optical and SAR images, maintaining a high matching success rate (SR) and accuracy under various conditions, including large-scale rotations, scale changes, and even perspective transformations.