A secure and reliable supply of energy is important for economic stability and even in social life. Increasing human population, industrialization, and rising living standards lead to increased electrical energy demand. Uncertainties in oil prices, shortage of fossil fuel reserves, and environmental pollution from conventional fuels leads solar energy as an alternative resource for electricity production. The share of installed photovoltaic (PV) capacity as a percent of total installed power generating capacity is increasing every year. In this study, an improved methodology to design large-scale PV power plant is proposed. The proposed methodology is performed for designing optimal configurations of PV power plants. The design methodology is performed using commercially available PV modules and inverters. In addition, solar radiation, ambient temperature, wind speed, shadow effect, and location and shape of plant field are taken into consideration as input parameters. The alternatives and parameters are evaluated with the purpose of minimizing the levelized cost of generated electricity (LCOE). The methodology includes the use of a genetic algorithm (GA) for determining the optimal number of PV modules and inverters, optimum tilt angle of PV modules, required installation area for the plant and optimum cable cross section and lengths. In the paper, the methodology is implemented, and case studies and results using pvsyst software for the same case studies are compared with each other.