The adjustment of the previously developed algebra-algorithmic tools towards the automated design and generation of programs that use neuroevolutionary algorithms is proposed. Neuroevolution is a set of machine learning techniques that apply evolutionary algorithms to facilitate the solving of complex tasks, such as games, robotics, and simulation of natural processes. The developed program design toolkit provides automated construction of high-level algorithm specifications represented in Glushkov’s system of algorithmic algebra and synthesis of corresponding programs based on implementation templates in a target programming language. The adjustment of the toolkit for designing neuroevolutionary algorithms consists in adding the descriptions and software implementations of the relevant elementary operators and predicates to a database of the toolkit. The use of the toolkit is illustrated by an example of designing and generating a program for the single-pole balancing problem, which applies the neuroevolutionary algorithm of the NEAT-Python library. The results of the experiment consisting in the execution of the program generated with the algebra-algorithmic toolkit are given.