Silicone rubber materials are commonly used as insulation materials under high-pressure conditions due to their excellent insulation properties. The breakdown field strength is an important electrical performance index， and there is a complex nonlinear relationship between the breakdown field strength and the material formula. Based on this， an efficient evaluation model based on genetic algorithm （GA） optimized extreme gradient boosting （XGBoost） algorithm is proposed. The model combines GA and XGBoost， and uses temperature， relative content of masterbatch， diameter of Al（OH）3 micropowder， relative content of Al（OH）3 and thickness as inputs to establish an improved XGBoost model to predict the breakdown field strength. The GA algorithm automatically selects the optimal parameters during the training process of the XGBoost model. The Pearson correlation coefficient is used to analyze the influencing factors. It can be seen that the thickness and temperature are the key factors affecting the breakdown field strength， while the influence of the masterbatch content， the diameter and relative content of Al（OH）3 micropowder is relatively small. The evaluation index of the commonly used regression model is compared with the proposed model. The coefficient of determination of the model can reach 0.953， and the root-mean-square error and mean absolute error are only 0.361 kV/mm and 0.168， respectively. The results show that the GA-XGBoost model can accurately predict the breakdown fieid strength of the material， which can provide a reference for studying the properties of silicone rubber materials and optimizing the material formulation.