Mehri and Alizadeh use the genetic algorithm (GA) to optimize field-programmable gate array (FPGA) architectural performance involving area, delay, and power consumption. Compared with the traditional geometric programming (GP) algorithm that requires the sweeping of parameters such as look-up table (LUT) size and configurable logic block (CLB) number to find the optimal values, the GA algorithm can (1) explore the FPGA architecture and optimize the parameters concurrently and (2) find these parameters two orders of magnitude faster than the GP framework. Although the GA algorithm has been applied in some physical designs, such as placement and routing of FPGAs, this is the first usage in FPGA architectural analysis. In addition, some performance metrics are also formulated and summarized in this paper, such as the area and delay models from Smith et al. [1], and the power models from Mehri and Alizadeh [2] that are referred to by the proposed algorithm. All of these models are very useful in abstract-level performance analysis, particularly at the beginning of some complex projects.