The rapid expansion of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has significantly increased the attack surface of such systems, making them vulnerable to a wide range of cyber threats. Traditional Intrusion Detection Systems (IDS) often fail to meet the stringent requirements of resource-constrained IoT environments due to their high computational cost and reliance on large labeled datasets. To address these challenges, this paper proposes a novel hybrid Intrusion Detection System that integrates a Quantum Genetic Algorithm (QGA) with Self-Supervised Learning (SSL). The QGA leverages quantum-inspired evolutionary operators to optimize feature selection and fine-tune model parameters, ensuring lightweight yet efficient detection in resource-limited networks. Meanwhile, SSL enables the system to learn robust representations from unlabeled data, thereby reducing dependency on manually labeled training sets. The proposed framework is evaluated on benchmark IoT intrusion datasets, demonstrating superior performance in terms of detection accuracy, false positive rate, and computational efficiency compared to conventional evolutionary and deep learning-based IDS models. The results highlight the potential of combining quantum-inspired optimization with self-supervised paradigms to design next-generation intrusion detection solutions for IoT and WSN environments.