Nowadays, the high cost of production and resources is the problem in the world, because of this, the issue of optimizing production to reduce the use of resources becomes acute. The research of the experiment at the initial stage makes it possible to reduce resource costs due to detailed analysis. For this we identify steps that we can simplify, which saves resources during production or research. Most often, experiments are multifactorial and related to the search for optimal conditions, selection ofthemost rational equipment and high-quality raw materials. There is a need to increase the effectiveness of experimental research. These researches allow us to study objects in detail, which provides the ability to obtain more information and offers conditions for their optimization.In the process of researching objects, it is necessary to build their mathematical models, which allow us to determine a rational ratio of parameters. Experiment planning allows for calculating the most effective order of performing experiments and studying the influence of individual factors on optimization criteria. The use of experimental planning methods helps in obtaining the maximum amount of useful information with minimal cost and time spent.This article examine&s the growing tree method for cost optimization of multifactor experimental plans. To confirm its functionality and effectiveness, a comparative analysis is conducted with existing optimization methods. The method is inspired by the evolution of grow&ing trees and includes the stages of planting and growth. An algorithm and software that implement this method have been developed. The software implementation of the algorithm is made with the help of the framework Angular.In the study of technologi&cal processes, the functionality and effectiveness of the method of growing trees for cost optimization of plans of multifactor experiments has been proven. It has been compared with the bacterial optimization method and the method based on the use o&f the Gray code. The object of research: the process of optimization of plans of multifactor experiments according to its cost. The subject of study: growing tree method for cost optimization of multifactor experimental plans and software implementin&g it.
