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Line personnel occupy the vast majority of positions in many organizations, which determines the importance of timely and successful filling of such vacancies. The search for candidates for such positions is carried out through mass recruitment, which is characterized by high labor intensity, budgetary and time constraints, and the need for regular repetition due to high staff turnover rates. The noted features make it impossible to carry out this process without the use of modern software. Since mass recruitment does not require finding the best candidate for each vacancy and is limited to searching for specialists based on formal criteria from their resume, the main share of labor and time costs falls on the primary selection of candidates. Existing software does not have sufficient functionality to effectively automate this process. Given the need to process large volumes of multidimensional data, they do not provide a comprehensive accounting of different types of candidate characteristics and automatic adjustment of selection criteria taking into account their priority for the vacancy being filled. To solve the problem, an automated method for forming a set of candidates for linear positions was developed. It is based on the integrated use of an adaptive neuro-fuzzy inference system and a bioinspired algorithm inspired by the behavior of a fish school. The developed hybrid method was implemented as a computer program using the Python language. The results of its testing showed the convergence of the optimization algorithm, and their comparison with manual selection confirmed the prospects for using it to solve tasks of mass recruitment of line personnel.