The net heat of combustion is one of the most important properties of flammable substances that can be used to estimate the potential fire hazards of chemicals once they ignite and burn. This study proposed a quantitative structure-property relationship model to predict the net heat of combustion of 308 organosilicon compounds from only the knowledge of their molecular structures. Various kinds of molecular descriptors, such as topological, charge, and geometric descriptors, were calculated to represent the molecular structures of organosilicon compounds. The genetic algorithm combined with multiple linear regression is employed to select optimal subset of descriptors that have significant contribution to the overall net heat of combustion property. The best resulted model is a three-variable multilinear model, with the root-mean-square error and average absolute error for the external test set being 176.8 and 111.2 kJ/mol, respectively. Model validation was also performed to check the stability and predictive capability of the presented model. The results showed that the presented model is a valid and predictive model. This study can provide a new way for predicting the net heat of combustion of organosilicon compounds for engineering.