General-purpose Monte Carlo sampler. We introduce a novel Quadratic Monte Carlo (QMC) technique that is more efficient in confining fitness landscapes than affine invariant method that relies on linear stretch moves. We compare how long it takes the ensembles of walkers in both methods to travel to the most relevant parameter region. Once there, we compare the autocorrelation time and error bars of the two methods. For a ring potential and the 2d Rosenbrock function, we find that our quadratic Monte Carlo technique is significantly more efficient. Furthermore we modified the walk moves by adding a scaling factor. Here we provide the C++ source code and examples so that this method can be applied elsewhere.