In Industrial Internet of Things applications (IIoT), fog computing has soared as a means to improve the quality of services provided to users through cloud computing, which has become overwhelmed by the massive flow of data. Transmitting all these amounts of data to the cloud and coming back with a response can cause high latency and requires high network bandwidth. The availability of sustainable energy sources for fog computing servers is one of the difficulties that the service providers can face in IIoT applications. The most important factor contributing to energy consumption on fog servers is task scheduling. In this paper, we suggest an energy-aware metaheuristic algorithm based on a Harris Hawks Optimization algorithm based on a Local Search strategy (HHOLS) for Task Scheduling in Fog Computing (TSFC) to improve the quality of services provided to the users in IIoT applications. At first, we describe the high virtualized layered fog computing model taking into account its heterogeneous architecture. The normalization and scaling phase aids the standard Harris hawks algorithm to solve the TSFC, which is discrete. Moreover, the swap mutation ameliorates the quality of the solutions due to its ability to balance the workloads among all virtual machines. For further improvements, a local search strategy is integrated with HHOLS. We compare HHOLS with other metaheuristics using various performance metrics, such as energy consumption, makespan, cost, flow time, and emission rate of carbon dioxide. The proposed algorithm gives superior results in comparison with other algorithms.