Optimization a viewpoint we take on a process where it is easy to predict properties of the outcome by supposing them to have been coerced to a target ("preference"). An optimization process moves the world into otherwise-improbable states by searching for actions and plans predicted to hit those otherwise low-probability targets. When a process is guided by some agent into some specific state or property, via the agent modeling and predicting the process and choosing on the basis of how the agent orders the predicted outcomes, we can say the agent prefers according to its expected-outcome-orderer.
That is: If you play Stockfish or Magnus Carlsen at chess, you will find it much easier to predict that they will win the chess game than where they will move next. To understand what will happen to the chess board, with respect to the property "Who won", it is much easier to grab at your abstract belief that Magnus Carlsen wants to win, than for you in your own mind to simulate Magnus Carlsen's thought process well enough to predict exactly where he moves. (Indeed, if you think Magnus Carlsen is a generally better chess player, you think yourself necessarily unable to predict his next moves in general! But this doesn't mean you can predict nothing about the chess game; you can predict Magnus Carlsen wins.)
Conversely, to predict in detail how far a ball will roll down a complicated mountain, you can do better by thinking about how the ball locally chooses a direction of steepest descent modulo momentum, until you predict where it will fall into a pit and get stuck. You can't usefully predict that the ball ends up at the bottom of the mountain by always choosing to locally roll in the direction that nonlocally avoids pits and takes a swift route to the bottom.
This is why it makes sense to regard Stockfish as more of an optimizer than a rolling ball, even if Stockfish is in principle knowable in even more detail than the rolling ball after all physical noise is taken into account. We can get a lot of mileage out of reasoning in our heads "Stockfish's local moves are understandable mainly through the nonlocal property of how they will later lead to a Stockfish-winning chessboard state" and not so much mileage by reasoning "Whichever way the ball just rolled is whichever way takes it to the bottom of the mountain fastest."
Natural selection similarly fits into this viewpoint as a naturally occurring optimizer. Through an implicit preference – better replicators – natural selection searches all the genetic landscape space and hit small targets: fitness-promoting mutations and combinations. A human being is a highly complex object with a low probability of being so reproductive-fitness-producing absent some kind of optimization - but natural selection is sufficient to explain this.
One way to think mathematically about optimization, like evidence, is in information-theoretic bits. The optimization power is the amount of surprise we would have in the result if there were no optimization process present. Therefore we take the base-two logarithm of the reciprocal of the probability of the result. A one-in-a-million solution (a solution so good relative to your preference ordering that it would take a million random / unoptimized tries to find something that good or better) can be said to have log_2(1,000,000) = 19.9 bits of optimization. Compared to a random configuration of matter, any artifact you see is going to be much more optimized than this.
The math describes only laws and general principles for reasoning about optimization; as with probability theory, you oftentimes can't apply the math directly.