The problem of developing a multifactor forecasting model using the tools of fuzzy set theory has been considered. It is proposed to represent each of the analyzed factors, which have the same source of occurrence, but different trajectories of evolution, in the form of a fuzzy time series. The groups of fuzzy logical dependencies on the basis of fuzzy time series for each trajectory have been created. The multifactor forecasting model is developed with the use of these groups of fuzzy logical dependencies, taking into account the operations of union and intersection. Optimal parameter values of the forecasting model, providing a minimum value of the average forecasting error rate, have been determined using a genetic algorithm. An example which confirms the effectiveness of the proposed approach to the development of a multifactor forecasting model has been given.