Cloud computing enables people to use computing sources (hardware, operating system, software, etc.) over a network. Virtualization technology makes it possible to share hardware resources (CPU, RAM, bandwidth, etc.) for more than one virtual machine (VM), hence virtualization technology is an indispensable part of cloud computing. VMs should be placed over physical machines (PMs) in cloud data centers that employ virtualization technology. While placing VMs, there are some points to be addressed simultaneously such as optimizing CPU, RAM and bandwidth usage while minimizing energy consumption. This is called virtual machine placement (VMP) problem. When more than one objective need to be optimized, multi-objective optimization algorithms are used. In this paper, we tackle the VMP problem by optimizing CPU utilization while minimizing energy consumption. For this purpose, four well-known multi-objective evolutionary algorithms were selected and compared their performance on CloudSim, an open source simulation software. Detailed simulation results for the selected algorithms under different criteria are presented.