The influence of SAR speckle noise makes it difficult for the existing state-of-the art algorithms to guarantee the repeatability rate of feature points when extracting them from optical and SAR images owing to the nonlinear radiation differences between optical and SAR remote sensing images， which consequently reduce the matching performance. To address the above problems， a Harris feature point extraction algorithm based on phase congruency moment feature is proposed. Firstly， blocking strategy was used to divide the input image into several image blocks； secondly， phase congruency intermediate moments were defined； then， phase congruency multi-moment maps were calculated for each image block； and finally， a voting strategy was designed on the phase congruency multi-moment maps. The feature points that appeared more than half of the time on the multi-moment image were selected as the final feature points. In this study， the simulated optical and SAR images were used as experimental data， and three different feature point detection algorithms were selected for comparison with the proposed algorithm. Experimental results showed that the proposed algorithm can overcome the influence of nonlinear radiation differences between optical and SAR remote sensing images and the SAR speckle noise， improving the repeatability rate of feature points effectively. The registration results on the real optical and SAR images showed that， compared with the other three algorithms， the matching points increased by 23， 26， and 35 pairs and the root mean square error decreased by 12.6%， 37.2%， and 40.8%， respectively. The performance of registration algorithm was improved effectively.