To improve the accuracy of fault detection in array antenna, an enhanced differential evolution-genetic algorithm (DE-GA) is proposed. This algorithm combines the advantages of genetic algorithm (GA) and differential evolution (DE) by employing a dual crossover strategy to help individuals escape local optima. An adaptive weighting mechanism further optimizes offspring selection, enhancing the algorithm’s sensitivity and adaptability to fault conditions. Applied to array antenna fault detection, the DE-GA algorithm models the array and optimizes its radiation pattern to match the known faulty pattern, allowing the faulty array’s amplitude to be estimated. Experiments show that compared with DE and GA, DE-GA reduces the fitness function value by 11.15% and 12.90%, the mean absolute error by 19.36% and 23.85%, the mean square error by 12.90% and 11.15%, and the maximum error by 12.30% and 13.18%. This demonstrates higher accuracy and improved approximation capabilities. Additionally, the algorithm maintains excellent stability with larger arrays, making it suitable for large-scale fault detection.