Multi-access edge computing has the ability to provide high bandwidth, and low latency, ensuring high efficiency in performing network operations and thus, it seems to be promising in the technical field. MEC allows processing and analysis of data at the network edges but it has finite number of resources which can be used. To overcome this restriction, a scheduling algorithm can be used by an orchestrator to deliver high quality services by choosing when and where each process should be executed. The scheduling algorithm must meet the expected outcome by utilizing lesser number of resources. This paper provides a scheduling algorithm containing two cooperative levels with an orchestrator layer acting at the center. The first level schedules local processes on the MEC servers and the next layer represents the orchestrator and allocates processes to nearby stations or cloud. Depending on latency and throughput, the processes are executed according to their priority. A resource optimization algorithm has also been proposed for extra performance. This offers a cost-efficient solution which provides good service availability. The proposed algorithm has a balanced wait time (Avg) and blocking percentage (Avg) of 2.37ms and 0.4 respectively. The blocking percentage is 1.65 times better than Shortest Job First Scheduling (SJFS) and 1.3 times better than Earliest Deadline First Scheduling (EDFS). The optimization algorithm can work on many kinds of network traffic models such as uniformly distributed and base stations with unbalanced loads.