The fog computing paradigm was introduced to overcome challenges that cannot be addressed by conventional cloud computing, such as the lower response latency for real-time applications. Task scheduling in fog environments sets forth more complexity using novel objectives beyond scheduling in the cloud. In this study, a task scheduling model with five common objectives and two latency metrics is presented. We propose a latency aware multi-objective multi-rank scheduling algorithm, LAMOMRank, for fog computing. The performance of our algorithm was compared with that of three well known multi-objective scheduling algorithms, Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA2) and Multi-objective Heterogeneous Earliest Finish Time (MOHEFT) algorithm, using three multi-objective metrics and two latency addressing metrics. We populate workload sets using Pegasus workflows and the DeFog benchmark to be distributed over two fog clusters generated with various Amazon Web Services instances. The empirical results validate the significance of our algorithm for better latency fronts including the response latency and task delivery time without performance degradation on multi-objective metrics.