There's no general rule for optimization. We often focus on optimizing performance because we have tools such as the Big O measure of complexity that show us whether or not an algorithm is an effective solution to a given problem. Optimizing storage is usually tackled separately: we can look at the steps in an algorithm and estimate the size of the storage required for the various storage structures.
In many cases, the two considerations are opposed. In some cases, an algorithm that has outstandingly good performance requires a large data structure. This algorithm can't scale without dramatic increases in the amount of storage required. Our goal is to design an algorithm that is reasonably fast and also uses an acceptable amount of storage.
We may have to spend time researching algorithmic alternatives to locate a way to make the space-time trade off...
