Modern spacecraft trajectory mission planning regularly involves Non-Linear Programming (NLP) problem formulations. As the problems being posed become more complex, scientists have adopted high performance computing methods such as parallel programming to significantly speed up the time-to-solution. Unfortunately, the NLP solvers at the core of many of the modern trajectory optimization methods are becoming a serial bottleneck, and the single largest point of solution slowdown. CU Aerospace in partnership with the University of Illinois at Urbana-Champaign (UIUC) has developed a novel, ground-up redesign of an NLP solver that takes advantage of high performance parallel computing called the Non-Linear PARallel Optimization Tool (NLPAROPT). NLPAROPT uses the Message Passing Interface (MPI) as well as Parallel Basic Linear Algebra (PBLAS) techniques to carry out traditional NLP solution methods in parallel. Preliminary tests have shown NLPAROPT's ability to reduce the runtime by orders of magnitude when compared to its serial counterpart. Applications to simple problems as well as a multiple shooting trajectory optimization test problem are demonstrated. There remains significant additional avenues for parallelism and improved robustness that should proffer further gains.