In this paper, we propose a technique to flexibly implement Genetic Algorithms (GAs) for various problems on FPGAs. For the purpose, we propose a common architecture for GA. The proposed architecture allows designers to easily implement a GA as a hardware circuit consisting of parallel pipelines which execute GA operations. The proposed architecture is scalable to increase the number of parallel pipelines. The architecture is applicable to various problems and allows designers to estimate the size of resulting circuits. We give a model for predicting the size of resulting circuits from given parameters. Based on the proposed method, we have implemented a tool to facilitate GA circuit design and development. Through experiments using Knapsack Problem and Traveling Salesman Problem (TSP), we show that the FPGA circuits synthesized based on the proposed method run much faster and consume much lower power than software implementation on a PC, and that our model can predict the size of the resulting circuit accurately enough.