With the wide deployment of mobile devices, a variety of different computation-intensive applications for mobile platforms, such as online games and virtual augmented reality, have emerged. However, due to the limited computation resources, mobile devices struggle to complete the required computation in time. One potential solution to the problem is mobile edge computing. Over the past years, there have been a series of studies on edge server placement. Most of the existing studies focus on the minimization of the delay between mobile devices and edge servers because many mobile applications are time-sensitive. However, workload balance is also an important metric because we do not prefer a scenario where some edge servers are idle while others are overloaded. In our research, we took both the delay and workload balance into account when we attempted to propose an effective edge server placement strategy. In addition, machine learning techniques were utilized to arrive at the appropriate placement method. Specifically, an innovative edge server placement strategy, which combines the advantages of cluster-based method and heuristic schemes, is proposed in the paper. Our experimental results indicate that, compared with the existing schemes, the proposed strategy can achieve lower communication delay and better workload balance simultaneously.