This paper describes how distributive computing along with statistical subsystem simulation can be applied to produce near production ready embedded vehicle software and calibrations. Coupling distributive computing and statistical simulation was first employed over a decade ago at General Motors to design and analyze propulsion subsystem hardware. Recently this method of simulation has been enhanced extending its capabilities to both test embedded vehicle code as well as develop calibrations. A primary advantage of this simulation technique is its ability to generate data from a statistically significant population of subsystems. The result is the acquisition of an optimal data set enabling the development of a robust design now including both embedded code and calibrations. Additionally it has been shown that there are significant economic advantages in terms of time and cost associated with this type of development when compared to traditional method. The following section will describe in detail using examples and data the advantages of this innovative approach to software testing and calibration.