The quality of the credit evaluation classifier can directly affect the profitability of credit financial institutions. The traditional grid search takes a lot of time for parameter optimization. Based on this, we propose an improved grid search to optimize the XGBoost (GS-XGBoost) personal credit evaluation algorithm. After using the feature selection based on random forest, the algorithm uses the improved grid search method to optimize the parameters of n_estimators and learning_rate in XGBoost to establish an evaluation model. We analyze the credit data selected from the UCI database to compare with support vector machines, random forests, logistic regression, neural networks, and unimproved XGBoost. Experimental results show that the F-score and G-mean values of the model are improved.