The identification of the geographic distribution of wildlife is fundamental in applied ecology, since it provides important information for subsequent analyses. However, the investigation of wildlife is often expensive and time consuming, especially for rare species and when using inefficient sampling designs. To determine target species more efficiently, we tried to apply model-based sampling using predictions from species distribution models (SDMs). We used black-necked (Grus nigricollis) and hooded (Grus monacha) cranes as two examples, and used the Random Forest algorithm combining the breeding location and environmental information to model the breeding geographic distribution of the two crane species. We extracted the relative index of occurrence (RIO) for the breeding locations (testing points, model-based sampling method), random point locations (random sampling method), and regular point locations (regular sampling method) from the prediction map. Then, we used boxplots and ANOVA to analyze these data; the results indicated breeding locations with higher RIOs, and a significant difference was found between the other two methods. Therefore, the model-based sampling method helped to reduce the size of the investigated areas and determine target species more effectively. To conclude, a species distribution model-based sampling method for fieldwork would help to increase our knowledge of rare species distributions. More generally, we recommend using this approach to support conservation plans.