This thesis focuses on modelling and Simulation of an optimized model for the power consumed, which is used to manage HVAC (Heating, Ventilation and Air-Conditioning) systems inside intelligent buildings using intelligent control of energy, as an application model into SCADA (Supervisory Control And Data Acquisition) systems. Fanger's comfort method was used to obtain the initial values of PMV (Predict Mean Vote), PPD (Predicted Percentage of Dissatisfied), and TA (Expected Air Temperature). These optimal and initial values were obtained using genetic algorithms criteria programmed with Matlab. PMV value is very close to zero (natural comfort level), and TA value is near 22C°, which is considered as an acceptable user's comfort levels. In order to obtain more accurate and optimal results, which are concern with PMV value; the heat lost and heat gained per unit time values were calculated as a new feature was added to Fanger's approach. The heat lost through walls per unit time was calculated depending on a set of inputs that can be obtained from Fanger's equations, as well as another set of inputs were used to find the heat transmission coefficient between walls. The calculations which are concern with the heat transmission coefficient between internal and external environments were done depending on several inputs and factors; part of them can be acquired and obtained via SCADA actuators and sensors, and the other part considered as constant values, these should feed as manually inputs to the designed model. The main feature of the designed real-time model is the prediction of the internal buildings environment, in order to be able to control HVAC systems indoor to obtain the optimum power consumed depending on optimized air temperature value obtained from genetic algorithm that could prevent the power used to reach its peak value, and to maintain the comfort user's environment according to ISO (International Organization for Standardization).