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Robust multi-user detection (MUD) methods based on space division multiple access (SDMA) techniques are essential to efficiently exploit the electromagnetic spectrum. In this paper, an adaptive Genetic Algorithm-based tool for SDMA-OFDM Systems (GASOS) is developed to improve the performance and computational complexity in cases of fully-loaded and overloaded multi-user scenarios. The data flow in GASOS is appropriate in pipelining and parallelization to reduce operational time. A new GASOS-based MUD hardware design for SDMA-OFDM systems is proposed using FPGA architecture. The design details are presented together with their planned operational modules. Resource utilization is optimized, and the total number of clock cycles required is found to be 15 initially, in addition to one clock cycle per member of algorithm population. A clock frequency of 100 MHz is used and implementation is carried out on Xilinx Ò Virtex-6 FPGA, built in the development platform ML605 edition with JTAG Hardware Co-simulation. According to the results obtained from the developed algorithm and implementation tools, a high number of users can be physically possible and provided with support. Real-time based implementation of MUD systems has the potential to play a major role in next-generation communication systems.