Options pricing is still an open area for exact results. Monte Carlo integration is the unique solution for especially complicated options. It is desired to control variability while implementing Monte Carlo techniques. In order to supply reliable results variance of simulation trials should be decreased. Importance Sampling is one of the variance reduction techniques commonly used in Monte Carlo applications. This study includes a research to gather the appropriate Importance Sampling density which gives the lowest variance. We illustrate the Importance Sampling method on financial options and calculate the value of options. By the same way, it is possible to calculate any expectation that cannot be calculated analytically. Numerical results indicate that longer tailed proposal distributions provide substantial decrease in the estimated variance.