Good software engineering practices (eg using good data structures, using appropriate algorithms, etc) is not optimization.
Software always requires tradeoffs (I don't consider "it does what it is supposed to" a trade off; that's more an invariant). In general, we put human consumption factors (readability, etc) high on the priority list, which is where it should be, usually.
Optimization is taking code with one set of value priorities and making a different set of priorities. Until you hit the real world, though, you won't know which priorities you need to value: is it hard drive throughput, raw processor cycles, or network latency? Regardless, in the end, you will generally have code that is less fit for humans, meaning the cost of maintenance will go up, so you want to ensure you are only optimizing what really needs to be optimized.
If "optimization" means "I didn't think about what data structure to use", you don't have an optimization problem. You have a software engineering problem.
Software always requires tradeoffs (I don't consider "it does what it is supposed to" a trade off; that's more an invariant). In general, we put human consumption factors (readability, etc) high on the priority list, which is where it should be, usually.
Optimization is taking code with one set of value priorities and making a different set of priorities. Until you hit the real world, though, you won't know which priorities you need to value: is it hard drive throughput, raw processor cycles, or network latency? Regardless, in the end, you will generally have code that is less fit for humans, meaning the cost of maintenance will go up, so you want to ensure you are only optimizing what really needs to be optimized.
If "optimization" means "I didn't think about what data structure to use", you don't have an optimization problem. You have a software engineering problem.