With the increasing of IoT and networking technology, the volume of data has increased, thus promoting the advent of edge computing which can satisfy the low-latency requirements for current application. The paradigms mentioned above need efficient scheduling of tasks and optimization of resources to optimally utilize them in a balanced environment where devices are heterogeneous and workloads vary with time. In this paper, we propose an innovative approach of task scheduling and optimization in the framework of edge computing using nature-based algorithm known as Slime Mold Algorithm (SMA). The reason we use SSA and SMA as in our proposed approach is they are capable to deal with the complex search space and have ability to converge at optimal solution. The authors give a comparative statement of these algorithms to traditional ones, demonstrating that the former has made some improvement in distribution load and r source allocation. We evaluate the proposed methods in realistic simulation scenarios to demonstrate how the algorithms improve latency and reliability of an edge computing network. Indicators formulated on these results show the combination of SSA and SMA with edge computing environments as an effective strategy to solve problems related to task scheduling and optimization.