The solution to chemical kinetic models for a particular reactor configuration is usually composed of a set of stiff ordinary differential equations (ODEs), where a number of rate parameters have to be estimated to fit with experimental observations. This presents a twofold challenge-first, to solve the stiff set of ODEs accurately and efficiently, and second, to estimate the model parameters precisely by optimizing the objective function. In recent years, stochastic optimization methods for parameter estimation have gained popularity over the classical optimization methods as the former do not require a reasonable initial guess and have the capability to escape local minima. In this study, we systematically examined 10 different stochastic optimization algorithms and evaluated their performance to estimate the model parameters for the previously developed propane oxidation mechanism, popularly known as the San Diego mechanism. In doing this, we developed an open source python package kinexns to efficiently solve the kinetic model using CVode solver, perform sensitivity analysis to determine important model parameters, and optimize the model parameters by using the different stochastic methods. The different algorithms we considered are Monte Carlo (MC), Latin hypercube sampling (LHS), maximum likelihood estimation (MLE), Markov chain Monte Carlo (MCMC), shuffled complex evolution algorithm (SCE-UA), simulated annealing (SA), robust parameter estimation (ROPE), artificial bee colony (ABC), fitness scaled chaotic artificial bee colony (FSCABC), and dynamically dimensioned search algorithm (DDS). The results indicated that the MLE and DDS provide more reliable parameter approximation among all of the algorithms evaluated.