Evolvable Hardware (EHW) is a new field about the use of Evolutionary Algorithms (EA) to synthesize a circuit. Genetic Algorithm (GA) is one of the typical EA. In traditional GA, the crossover is one-point crossover or two-point crossover. One-point crossover and two-point crossover change the genes of individuals too many in one time and they are not flexible, so it may lose some useful genes. In this paper, we propose the novel cell crossover. The cell crossover can change genes more flexibly and enhance more diversification to search spaces than one-point crossover and two-point crossover, so that we can find better solution. We propose optimal circuit design by using GA with cell crossover (GAcc), and with fitness function composed of circuit complexity, power and signal delay. Simulation results show GAcc is superior to traditional GA in point of the best elite fitness, the average value of fitness of correct circuits and the number of correct circuits. The best optimal circuit generated by GAcc is 27.9% better in evaluating value than that by GA with one-point crossover.