In this paper are being considered the aspects of two variables function optimization problem solving, which, in general, is poly-extremal and undifferentiated. The classic methods of continuous optimization are not applicable in this case. One of the most commonly used methods of solving this problem is evolutionary algorithms, which can be divided into two classes. The first class includes algorithms where a potential offspring-solution is generated by two parent-solutions solutions, in the second case, the offspring-solution is generated by one parent-solution. There is deformed star method proposed where the population of parental solutions is 3, 4, and 5 point groups. The application of proposed method is shown to solve the optimization problem of fire monitoring system for buildings, which minimizes the time of its operation. The buildings where fire load can be both permanent and variable are considered. Such buildings include concert halls, nightclubs, supermarkets, logistics facilities and more. Fires at such buildings result in human sacrifice and serious material loss. Timely activation of the fire alarm system have great importance. The objective function of the problem is determined by the distance from the horizontal projections of the detectors to the sources of fire and the probability of triggering the detectors. The solution is optimizing location of fire detectors, taking into account their number and the fire load of the room. The advantages of the developed method over genetic algorithms, evolutionary strategies and differential evolution as the most typical evolutionary algorithms are shown. Numerical experiments were carried out, which showed the increased accuracy of calculations and the increased speed of method convergence.