A spectrum unfolding technique GAMCD (Genetic Algorithm and Monte Carlo based spectrum Deconvolution) has been developed using the genetic algorithm methodology with in the frame work of Monte Carlo simulations. Each Monte Carlo history starts with initial solution vectors (population) as randomly generated points in the hyper dimensional solutions pace that are related to the measured data  by the response matrix of the detection system. The transition of the solution points in the solutions pace  from one generation to another are governed by the genetic algorithm methodology using the techniques  of cross-over(mating) and mutation in a probabilistic manner adding new solution points to the population. The population size is kept constant by discarding solutions having lesser fitness values (larger differences between measured and calculated results). Solution shaving the highest fitness value at the end of each Monte Carlo history are averaged over all histories to obtain the final spectral solution. The present method shows promising results in neutron spectrum unfolding for both under-determined and over-determined problems with simulated test data as well as measured data when compared with some existing unfolding codes. An attractive advantage of the present method is the independence of the final spectra from the initial guess spectra.