Mobile cloud computing (MCC) combines the portability of mobile devices with cloud data centers to provide advanced services. MCC serves us in various ways in our daily lives, including multimedia streaming, mobile gaming, mobile corporate apps, and data-intensive mobile applications such as augmented reality and virtual reality. Among the several challenges involved in achieving the best performance for this service, job scheduling emerges as a particularly critical one. User satisfaction, cloud service provider requirements, user priority, cloud provider's resource limitation, user deadline, cloud provider's energy consumption, etc., are the main constraints while maintaining job scheduling in mobile cloud computing. To improve the quality of service (QoS) and achieve the effectiveness of job scheduling, we have proposed a multi-objective model to balance the situation between user gratification and the cloud service provider's demand. To optimize the cost efficiency of the virtual machine, two types of jobs represent unconstrained and constrained jobs in the cloud data center. The shortest execution first scheduling (SEFS) algorithm is applied for the unconstrained job, and efficient deadline and priority job scheduling (EDPS) algorithm is applied for the constrained job. Our proposed algorithm improves the performance of the existing state-of-the-art algorithms. Reducing the execution time of jobs and minimizing the resource consumption of cloud providers are the improvements of our proposed algorithm.