Within the framework of the understanding of the forest and its management, significant changes have taken place in recent years. Here is an increasing demand on a planning process that includes a numerous stakeholders and many of forest information from different spatial levels. It is also necessary to consider with risk and uncertainty that inherently belongs to the management of natural resources. For this reason, it is necessary to create a forest management concept that would allow creating adaptable management strategy and the calculation with nearby unlimited variables in the decision-making process. For this purpose, the multicriterial decision-making (MCDM) method, which combined with forest growth simulators, provides a suitable set of methods for achieving these goals.The aim of the thesis is i) to outline a brief overview of MCDM methods suitable for optimization of forest management, ii) to present a possible approach to the selection of ecosystem services indicators and the creation of input data for optimization, and iii) to present 5 heuristic methods that could be suitable for optimizing forest treatment for the whole forest management units (FMU). The entire concept of optimization based on the data commonly available in outputs of growth simulator SIBYLA and on the growth table concept, allowing almost immediate tending optimization of any FMU or forest stand in Slovakia.Main results show, that the optimization of tending of forest stand for whole FMU is quite simple optimization problem with good structurability and without spatial constrains and therefore for optimization are better heuristic algorithm as random ascent, simulated annealing or HERO then evolutionary heuristic algorithm as SPEA-II or NSGA-II.