The tremendous expansion of the Internet of Things (IoTs) has generated an enormous volume of near and remote sensing data, which is increasing with the emergence of new solutions for sustainable environments. Cloud computing is typically used to help resource-constrained IoT sensing devices. However, the cloud servers are placed deep within the core network, a long way from the IoT, introducing immense data transactions. These transactions require heavy electricity consumption and release harmful 𝐶𝑂2
to the environment. A distributed computing environment located at the edge of the network named fog computing has been promoted to reduce the limitation of cloud computing for IoT applications. Fog computing potentially processes real-time and delay-sensitive data, and it reduces the traffic, which minimizes the energy consumption. The additional energy consumption can be reduced by implementing an energy-aware task scheduling, which decides on the execution of tasks at cloud or fog nodes on the basis of minimum completion time, cost, and energy consumption. In this paper, an algorithm called energy-efficient makespan cost-aware scheduling (EMCS) is proposed using an evolutionary strategy to optimize the execution time, cost, and energy consumption. The performance of this work is evaluated using extensive simulations. Results show that EMCS is 67.1% better than cost makespan-aware scheduling (CMaS), 58.79% better than Heterogeneous Earliest Finish Time (HEFT), 54.68% better than Bees Life Algorithm (BLA) and 47.81% better than Evolutionary Task Scheduling (ETS) in terms of makespan. Comparing the cost of the EMCS model, it uses 62.4% less cost than CMaS, 26.41% less than BLA, and 6.7% less than ETS. When comparing energy consumption, EMCS consumes 11.55% less than CMaS, 4.75% less than BLA and 3.19% less than ETS. Results also show that with an increase in the number of fog and cloud nodes, the balance between cloud and fog nodes gives better performance in terms of makespan, cost, and energy consumption.