Abstract : Cloud computing is a rapidly growing services business in today’s IT market, and its growth is producing many challenges for service providers. One primary concern is to utilize the cloud resources efficiently, i.e. to maximize utilization while maintaining the quality of service and minimizing energy consumption. Virtual machine placement (VMP) is a helpful key approach to achieve these goals. On the other hand, a multi-objective optimization (MOO) problem has become a critical research area. In order to find an optimal front and on-dominated set, it may require significant computing efforts. In this thesis, the MOO and VMP problem is addressed, which is considered to be NP-hard problem. The problem is modeled as a multi-objective optimization problem, considering energy consumption and resource utilization as objectives subject to predefined constraints. We adapted nature-inspired algorithms, such as non dominated sorting genetic algorithm II (NSGA-II) and multi-objective non-dominated sorting firefly algorithm (MONSFA), to solve the underlying problem. Experiments were done to evaluate the performance of both algorithms, using different metrics.