Edge computing is a promising technique for 5G networks to collect a wide range of environmental information from mobile devices and return real-time feedbacks to the mobile users. Generally, the edge servers (ESs) are both contributing in macro-base station (MABS) sites for large-scale resource provisioning and micro-base station (MIBS) sites for light-weighted resource response. However, to lower the investment of construing the edge computing systems in the MIBS sites, limited number of ESs are employed, since there is an intensive distribution of MIBSs in 5G networks. Thus, it remains challenging to guarantee the execution efficiency of the edge services and the overall performance of the edge computing systems with limited ESs. In view of this challenge, a load-aware edge server placement method, named LESP, is devised for mobile edge computing in 5G networks. Technically, a decision tree is constructed to identify the MIBSs served by a definite ES and confirm the data transmission routes across MIBSs. Then, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain the balanced ES placement strategies. Furthermore, simple additive weighting (SAW) and multiple criteria decision making (MCDM) techniques are leveraged to recognize the optimal ES placement strategy. Finally, the experimental evaluations are implemented and the observed simulation results verify the efficiency and effectiveness of LESP.