<p>Solid oxide fuel cells (SOFCs) are an advanced green energy technology that efficiently harnesses H₂ and is very important in transforming the energy landscape of vehicles. However, traditional SOFCs often struggle to meet vehicle performance demands. For anode-supported automotive SOFCs, the gradient anode design is considered an important structure that is expected to enhance the comprehensive performance. Although the optimization of anodes with gradient porosity and particle size has been well studied, the optimization of anodes with gradient component distributions has not been adequately researched. The performance of SOFC with homogenous anode components is well researched. The study analyzed the effect of the number of anode functional layers (AFLs) on SOFC performance, and the component distribution of the layered AFL was optimized using a combination of a back-propagation neural network (BPNN) and a genetic algorithm (GA). Finally, the distribution of the continuous gradient components in the AFL was optimized using the same methodology. The results show that the variation of component distribution in the anode support layer (ASL) has minimal impact on the SOFC performance; however, the SOFC performance is significantly enhanced when the anode is structured with two layers (<i>l</i> = 2). The maximum power density (<i>P</i>_max) of the layered anode (<i>l</i> = 2) with optimized gradient components increased by 10.63% compared to a homogeneous anode structure. This improvement is observed when the electron conductor volume fractions of AFL1 and AFL2 are set at 64.37% and 46.50%, respectively, and when the components of AFL are continuously distributed according to a power function, the <i>P</i>_max surpasses all other tested conditions.</p>