Adversarial training is regarded as a core defense mechanism for enhancing the robustness of deep models, yet its inherent limitations significantly constrain its effectiveness in practical applications. Traditional adversarial training methods rely on fixed attack patterns to generate adversarial examples (AEs), leading to insufficient sample diversity, limited generalization capabilities, and difficulties in achieving an effective balance between robustness and clean accuracy. More crucially, existing adversarial training frameworks lack adaptive control over the training process, resulting in the robust overfitting phenomenon. To address these challenges, an evolutionary optimization-based adaptive adversarial training framework is proposed, named trade-off robustness and accuracy via adaptive strategy optimization (TRA²SO). It innovatively integrates a genetic algorithm into adversarial training and achieves progressive complexity escalation in AE generation through dynamic adjustment of attack strategies across different training phases. This mechanism not only enhances sample diversity but also effectively suppresses overfitting risks through early stopping enabled by strategy optimization records. Experiments on CIFAR series datasets demonstrate that, compared with traditional adversarial training methods, the proposed TRA²SO framework maintains baseline classification performance while improving robustness against multiple attack paradigms and accelerating training convergence. This study provides new insights into the robustness-accuracy trade-off in adversarial training, offering significant practical value for building trustworthy deep learning systems.