Aiming to investigate the disadvantage of the optimization algorithm of membrane computing (a P system) in which it is difficult to take advantage of parallelism in MATLAB, leading to a slow optimization speed, a digital-specific hardware solution (field-programmable gate array, FPGA) is proposed to design and implement the single-cell-membrane algorithm (SCA). Because the SCA achieves extensive global searches by the symmetric processing of the solution set, with independent and symmetrically distributed submembrane structures, the FPGA-hardware-based design of the SCA system includes a control module, an HSP module, an initial value module, a fitness module, a random number module, and multiple submembrane modules with symmetrical structures. This research utilizes the inherent parallel characteristics of the FPGA to achieve parallel computations of multiple submembrane modules with a symmetric structure inside the SCA, and it achieves a high degree of parallelism of rules inside the modules by using a non-blocking allocation. This study uses the benchmark Sphere function to verify the performance of the FPGA-designed SCA system. The experimental results show that, when the FPGA platform and the MATLAB platform obtain a similar calculation accuracy, the average time-consuming of the FPGA is 0.00041 s, and the average time-consuming of MATLAB is 0.0122 s, and the calculation speed is improved by nearly 40 times. This study uses the FPGA design to implement the SCA, and it verifies the advantages of the membrane-computing maximum-parallelism theory and distributed structures in computing speed. The realization platform of membrane computing is expanded, which provides a theoretical basis for further development of the distributed computing model of population cells.