Visual Sensor Networks (VSNs) have become a key domain within sensor-based distributed intelligent systems, presenting distinct challenges in ensuring the quality of service (QoS) by guaranteeing coverage requirements and extending network lifetime. Among these challenges, the Heterogeneous Coverage of Targets (HCTs) problem stands out, where coverage requirements differ for each target based on its significance, and sensor orientation is restricted to discrete directions. This paper solves the HCTs problem under two critical scenarios: over-provisioned environments, where a sufficient number of sensors can meet all coverage demands, and under-provisioned environments, where sensor resources fall short of fulfilling the required coverage. Leveraging the success of evolutionary algorithms in finding near-optimal solutions, we propose an enhanced version of the traditional L-SHADE algorithm, namely Hybrid LSHADE with novel Local Search strategy (H-LSHADE). Our improvements include a specialized mutation strategy to maintain search space diversity and a local search mechanism to refine solutions. Comprehensive simulations demonstrate the proposed approach’s efficiency and effectiveness compared to existing methods.