The installation of photovoltaic (PV) plants has been expanding rapidly across the world during the last years. In this paper, a methodology for the design optimization of PV plants is presented, which, in contrast to the conventional PV plant design approaches, is suitable to be executed using high time-resolution (i.e., 1-min-average) values of the meteorological input data. Due to the nonlinear operation of the devices comprising a PV plant, this allows for the accurate estimation of the PV plant performance during its operational lifetime period. A parallel processing-based implementation of genetic algorithms has been employed, which, compared to the serial execution, provides the ability to accomplish the proposed optimal design procedure in a considerably shorter time interval. The design optimization results confirm that the proposed method successfully accounts for both the meteorological conditions and the operational characteristics of the PV plant components and incorporates their impact on the PV plant energy production and cost in the design process. Thus, the proposed optimization method allows for optimum design of PV systems, which will provide maximum economic profit during their lifetime period.