  We present preliminary results of a pulsar population synthesis of normal
pulsars from the Galactic disk using a Markov Chain Monte Carlo method to
better understand the parameter space of the assumed model. We use the Kuiper
test, similar to the Kolmogorov-Smirnov test, to compare the cumulative
distributions of chosen observables of detected radio pulsars with those
simulated for various parameters. Our code simulates pulsars at birth using
Monte Carlo techniques and evolves them to the present assuming initial
spatial, kick velocity, magnetic field, and period distributions. Pulsars are
spun down to the present, given radio and gamma-ray emission characteristics,
filtered through ten selected radio surveys, and a {\it Fermi} all-sky
threshold map. Each chain begins with a different random seed and searches a
ten-dimensional parameter space for regions of high probability for a total of
one thousand different simulations before ending. The code investigates both
the "large world" as well as the "small world" of the parameter space. We apply
the K-means clustering algorithm to verify if the chains reveal a single or
multiple regions of significance. The outcome of the combined set of chains is
the weighted average and deviation of each of the ten parameters describing the
model. While the model reproduces reasonably well the detected distributions of
normal radio pulsars, it does not replicate the predicted detected $\dot P - P$
distribution of {\it Fermi} pulsars. The simulations do not produce sufficient
numbers of young, high-$\dot E$ pulsars in the Galactic plane.
