Accurate detection and segmentation of lesions induced by High-Intensity Focused Ultrasound (HIFU) in medical imaging remain significant challenges in automated disease diagnosis. Traditional methods heavily rely on labeled data, which is often scarce, expensive, and time-consuming to obtain. Moreover, existing approaches frequently struggle with variations in medical data and the limited availability of annotated datasets, leading to suboptimal performance. To address these challenges, this paper introduces an innovative framework called the Optimized Multi-Task Contrastive Learning Framework (OMCLF), which leverages self-supervised learning (SSL) and genetic algorithms (GA) to enhance HIFU lesion detection and segmentation. OMCLF integrates classification and segmentation into a unified model, utilizing a shared backbone to extract common features. The framework systematically optimizes feature representations, hyperparameters, and data augmentation strategies tailored for medical imaging, ensuring that critical information, such as lesion details, is preserved. By employing a genetic algorithm, OMCLF explores and optimizes augmentation techniques suitable for medical data, avoiding distortions that could compromise diagnostic accuracy. Experimental results demonstrate that OMCLF outperforms single-task methods in both classification and segmentation tasks while significantly reducing dependency on labeled data. Specifically, OMCLF achieves an accuracy of 93.3% in lesion detection and a Dice score of 92.5% in segmentation, surpassing state-of-the-art methods such as SimCLR and MoCo. The proposed approach achieves superior accuracy in identifying and delineating HIFU-induced lesions, marking a substantial advancement in medical image interpretation and automated diagnosis. OMCLF represents a significant step forward in the evolutionary optimization of self-supervised learning, with potential applications across various medical imaging domains.