In the context of research and development, it is key to achieve accurate and reliable results. However, often to obtain these results, a large number of experiments must be performed, which can significantly extend the research time and increase computational requirements. The solution to these problems may be efficient experimental planning, which allows for a reduction in the number of trials and optimization of the process. This article provides an insight into Central Composite Design (CCD) and its use in simulation experiments. We introduce various types of CCD designs, such as CCC (Central Composite Circumscribed), CCF (Central Composite Face centered), and CCI (Central Composite Inscribed), and analyze their use in creating second-order regression models. We also discuss the specific advantages and disadvantages of these approaches, as well as their possible alternatives, such as the Draper-Lin CCD design.