We present a new pattern generation method for the Logical Analysis of Data (LAD) that is based on genetic algorithms. The original LAD method is often too slow and struggles to find patterns with many features. Our approach makes it faster and allows the model to find more complex patterns, even if it may not always find the perfect ones. We tested this method in credit card fraud detection, which is the process of identifying hidden or fraudulent transactions among many real ones. This is important because missing fraud or falsely labeling real transactions can cause serious problems. Our results show that the proposed method demonstrates superior performance across various dataset configurations. On a 3:1 sampled dataset, it achieves 97.46% accuracy, 97.94% precision, 92.23% recall, and a 95% F1-score, outperforming existing models. When tested on the original imbalanced dataset, it improves the F1-score to 67.21%, and with SMOTE balancing, it further reaches 96.08% accuracy and a 95.48% F1-score. These consistent results highlight the effectiveness.