The current thesis deals with the development of a computational framework for performing plasma chemistry simulations and their uncertainty quantification analysis by suitably combining and extending existing open source computational tools. A plasma chemistry solver is implemented in the OpenFOAM C++ solver suite. The OpenFOAM plasma chemistry application solves the species conservation equations and the electron energy equation by accounting suitably for various production and loss terms based on the provided reaction set. The OpenFOAM solver is initially used to study a simple two-reaction zero-dimensional argon plasma with one ionization reaction and one recombination reaction. The results are verified by comparing with ZDPlaskin (an existing plasma chemistry solver that is restricted to zero-dimensional simulations). The influence of a 5 % uncertainty on the rate constants is quantified and it is shown that the ionization reaction affects the entire time history whereas the recombination reaction only affects long-time behavior and steady state values of the species number densities. The OpenFOAM solver is then applied to a carbon dioxide dielectric barrier discharge plasma relevant to greenhouse gas reforming. The filamentary nature of dielectric barrier discharge plasmas is modeled by assuming a triangular pulse (pulse width of several nanoseconds) for power input followed by an afterglow period with zero input power. A reduced chemistry set comprising of 9 species and 17 reactions is used to obtain the time history of species number densities and electron temperature for various peak input power, initial electron number density, and time step and the results are verified using previously published work. A formal sensitivity analysis is performed by interfacing the OpenFOAM solver to DAKOTA (an open source framework for uncertainty quantification). The contribution of each reaction in determining the number density of a particular species is quantified by computing the ratio of standard deviation to mean for an uncertainty of 20 % for each reaction rate coefficient. The interplay predicted by the automated computational framework is along expected lines thereby demonstrating the feasibility of utilizing it for problems involving a more complex chemistry set.