Nowadays, with the advent of various communication technologies such as Internet of Things (IoT), a large volume of data is produced and needs to be processed in real-time. Fog computing is an appropriate solution to address the requirements of different types of IoT applications. In most cases, IoT applications consists a set of dependent task which can be separately processed in the heterogeneous Fog environment. Scheduling such dependent tasks in Fog environment is an NP-hard problem which needs a long time to be solved,making it unsuitable for real-time applications. In addition, reducing response time and energy consumption in Fog computing is an essential issue which should be taken into account in task scheduling algorithm. To address these challenges, we aim to propose a multiobjective task scheduling model to jointly improve energy efficiency and response time. To solve the model, we also propose an intelligent solution named IETIF which combines and levergaes the benefits of simulated annealing and NSGA-III algorithms. Simulation results show that IETIF outperforms the other state-of-the-art methods in terms of energy, response time and speedup.