Accurate and interpretable employee performance evaluation is essential for effective organizational decisionmaking. This work proposes a novel explainable classification framework, termed Self-Adaptive Genetic Boosted eXplainable Framework (SAGB-XSHAP), which integrates eXtreme Gradient Boosting (XGB) with a self-adaptive genetic optimization strategy and SHAP (SHapley Additive exPlanations) – based explanation stability analysis. The proposed approach jointly evolves feature interaction structures and model hyperparameters while explicitly penalizing unstable feature attributions during optimization. Unlike conventional methods that treat interpretability as a post-processing step, the proposed framework embeds explanation consistency directly into the learning process. Experimental evaluation on a structured employee performance dataset demonstrates that the SAGBXSHAP framework accomplishes a classification accuracy of 98%, outperforming traditional Machine Learning (ML) and standard boosting-based approaches. Furthermore, SHAP-based analysis reveals consistent and meaningful influence patterns for key employee attributes such as salary, overtime, and job involvement, enabling transparent and reliable performance assessment. The results confirm that the SAGB-XSHAP framework precisely balances predictive accuracy with explanation stability, making it acceptable for practical human resource analytics implementations.