Local optima can be seen on graphs like this:
Every peak besides the big one is a local optima. It’s a point where moving in any direction (left or right) gives you a worse amount. You can only go down if you make a small change. But it’s not a global optima b/c a different point is way better (the big peak), so it’s possible to move along the graph and come out ahead, but you have to go a decent distance to do that (leave the local area).
Another way to think of it is climbing a mountain. IIRC Dawkins gave this example in Climbing Mount Improbable. Mountains have places where you aren’t at the very top but any direction you go is down. Those are local optima. The only way to get higher up the mountain is to go down the mountain some first. This is relevant to how evolution works (animal genes can get stuck at local optima because if they go down at all their survival and replication chances are worse).