This work presents the main stages of the software implementation of two heuristic optimization methods - the genetic algorithm and the simulated annealing method. Compared to other classical approaches, these heuristic methods allow working with large search spaces and complex evaluation functions, facilitating the guaranteed achievement of the global optimum solution. The software implementation of both algorithms has been integrated into the computational core of a previously developed information system, which serves as a SaaS platform for organizing comprehensive studies of complex physicochemical processes. The integration of new software modules into the information system enables the utilization of cloud resources for solving optimization problems. The computational part of the heuristic methods is written in Python. Separate dialog windows have been created for task formulation, reaction kinetic scheme definition, and main algorithm parameters, shaping the content of the web interface of this system. The main stages of interaction with the interface part of the information system and its capabilities for solving problems with subsequent visualization of results are demonstrated using the example of optimizing the aminomethylation process of thiols with tetramethylmethanediamine.