Dry reforming of methane attracts much attention in order to convert the two greenhouse gases simultaneously to syngas. Preparation parameters of the citric acid method were surveyed to prepare a Co-MgO catalyst with a long life using the design of experiment (DOE), an artificial neural network (ANN), and a grid search (GS). The preparation parameters such as Co loading, amount of citric acid, calcination temperature, and pelletization pressure were determined according to an L-9 orthogonal array. After the catalytic activity was measured in a conventional fixed-bed reactor under pressure, a good fitting of the simple power-law equation (SPLE) to the activity change was obtained. The preparation parameters and the resultant SPLE parameters were used for the training of the ANN. The optimum was determined by a GS and verified experimentally to be stable. The combination of SPLE parameters by DOE, ANN, and GS was found to be a useful tool for the development of the catalyst.