Strong barrier coverage (SBC) is a critical requirement for IoT heterogeneous wireless sensor networks (IoT-HeWSNs), particularly in applications such as border surveillance, environmental monitoring, and intrusion detection. The achievement of optimal SBC in IoT-HeWSNs presents significant challenges due to the diversity of sensor capabilities, the conflicting nature of multiple objectives, and constraints on energy efficiency and deployment costs. Traditional methods often address the SBC problem using single-objective optimization, focusing on energy consumption, sensor deployment costs, or isolation coverage efficiency. To overcome these limitations, we formulate the multi-objective optimization problem for strong barrier coverage (MOO-SBC) and propose advanced optimization methods that simultaneously optimize sensor count, energy usage, and network coverage. Our approach, termed WS-SBC, employs state-of-the-art evolutionary algorithms to achieve superior trade-offs among these objectives. Extensive simulations demonstrate significant reductions in energy consumption and sensor count, along with ensured strong barrier coverage. The proposed method provides an efficient solution that balances energy consumption and the number of sensors. The achieved results improve IoT-HeWSN performance, offering practical applicability in real-world scenarios that require robust and cost-effective coverage solutions.