In recent years, operators have been constantly upgrading network capacity by deploying extra energy-consuming network elements due to the rapid expansion of high-bandwidth applications and the ongoing growth of Internet traffic. Optical transmission is also based on wavelength division multiplexing, which uses fixed channel spacing for a single or limited set of data rates. Power utilization is inefficient due to the constant channel spacing, which has an impact on operational costs and greenhouse gas emissions. In line of the aforementioned issues, the optical transport network is becoming the most energy-intensive segment. As a result, energy consumption is one factor that needs optimization in telecommunications networks. To reduce energy consumption in optical transport networks, many researchers employ optimization methods. They apply a single optimization algorithm and arrive at a conclusion based on the results of that algorithm. However, the performance of the algorithms varies from one to the next, making it difficult to rely on a single algorithm's output. The goal of this thesis is to compare and contrast the performance of three different evolutionary optimization techniques Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) in the energy-aware routing of the optical transport network. The problem, Energy-aware routing, is modeled as MILP using MATLAB. The model is passed to PSO, GA, and DE for optimization. These three algorithms are evaluated in terms of fitness finding, computational complexity, and convergence rate as performance metrics based on their output. Finally, under low traffic demand (100Gbps-5.6Tbps), PSO outperforms both GA and DE However, GA outperforms both PSO and DE in high-traffic (5.6Tbps-10Tbps) situations in terms of energy savings. PSO outperforms GA and DE in terms of computational complexity, taking 68.79% and 26.52% less time respectively. As a result, GA is more practical for high-traffic services, whereas PSO is for low-traffic ones.