We discuss line search algorithms - algorithms used to figure out how far in a given direction to travel to best minimize a function - and give two major examples. From there, we look at how several current optimization routines adapt line searches to handle simple constraints. Then, we introduce a new method that combines the advantages of two different strategies and show its benefits using numerical results.