The technological advancement proposed is a novel large-scale Noninear Parallel OPtimization Tool (NLPAROPT). This software package will eliminate the computational bottleneck suffered by many standard NASA-utilized analysis tools such as GMAT, EMTG and NASTRAN. Currently these programs rely on serial nonlinear programming solvers such as the Sparse Nonlinear OPTimizer (SNOPT), despite the fact that their own codebases support full parallelization. The same is true for tools used in other industries for applications such as electric power grid optimization, nuclear reactor control and stock market analysis. The NLPAROPT algorithm can be quickly incorporated into these existing software solutions via a user-friendly interface and will offer an instant runtime reduction for very large-scale optimization problems. Irrespective of runtime gains, Phase I analysis has shown that the NLPAROPT algorithm is capable of outperforming industry standard serial solvers such as SNOPT for tested problems, including complex trajectory design problems. The Phase I effort has also identified several potential computational research avenues that, once completed in Phase II, will result in massive execution speed increases, further improving the attractiveness of this new parallel algorithm.