Abstract: Taking the southeastern Tibet Region as the research object, the paper analyzes the landslide susceptibility based on optimized random forest(RF) model, and discusses the main influencing factors of landslides. With field survey, remote sensing data analysis and literature review, the study screens the landslide and non-landslide samples systematically, and optimizes the landslide and non-landslide sample screening methods of models, selection of impact factors, application of connection approach and hyperparameters. The random forest model (multi-objective optimization) is optimized by non-dominated sorting genetic algorithm (NSGA-Ⅱ) and compared with RF-GA mode (single objective optimization). The four indexes of optimal accuracy, recall rate, precision rate and F1 are increased by 3.3%, 8.7%, 3.2% and 1.9%, respectively, compared with the RF-GA model. Besides, the high, relatively high and medium susceptible areas increased by 2.7%, 3.1% and 1.2%, respectively, in terms of landslide susceptibility zoning. The high accuracy of RF-NSGA-Ⅱ model is verified (AUC=0.877) by drawing ROC curve and calculating AUC value. The results show that the landslide susceptible areas in southeastern Tibet are mainly concentrated in the intersection of Yigong Zangbo River and Palong Zangbo River and the big bend area of Yarlung Zangbo River. In the importance ranking of landslide impact factors, distance from road, elevation and distance from river take up the top places, as complex geological structures, densely developed faults and influence of long-term tectonic activities cause frequent landslides in such areas, especially in high susceptible areas with criss-crossing faults, broken rocks and developed joints.