This thesis deals with determination of Resistance (R) value and Heat Capacity of building envelopes in the commonly found residential architecture in Holland i.e. pitched roof houses. The aim of this thesis is to find an easy to implement novel method which gives reasonably accurate results. In the absence of actual sensor data, the data-sets are obtained by modelling the buildings in EnergyPlus, with the help of Design Builder which provides the specifications of the buildings and its energy systems. Data-sets obtained via EnergyPlus provide the heat demand of the buildings. These data-sets, which are considered to be an approximation of actual data that would be available in future, is fed into MATLAB to perform the inverse modelling using Genetic Algorithms (GA) which estimates the unknown parameters by fitting the energy demand curve. Genetic Algorithms are known to converge to the global optimum unlike other regression techniques and parameter estimation methods. The objective function of GA is derived from the most accurate thermal network model. The equation is an energy balance of the room for the indoor temperature node which constitutes transmission losses, ventilation losses, solar gain and internal heat gain.The working and behaviour of Genetic Algorithms with varying optimisation parameters are thoroughly studied to make a comprehensive report on the scope of the Algorithm.