If all available time is spent optimizing clearly that would be suboptimal since there would be no time left to actually engage in any particular process pursuant to what we value. So the optimal level of optimization is always suboptimal.

I find this confusing because I think it's (unintentionally) equivocating on what optimizing means.

Naively my response is that if you find you spent too much time trying to optimize for something, you did a suboptimal amount of optimizing. But this wouldn't mean the "optimal level of optimization is suboptimal", it would mean "optimizing only for goal B is suboptimal relative to goal A".

Programmer often talk about this problem as premature optimization: writing code that is optimal for some local concern but comes at the cost of effecient allocation of effort towards the larger goal.

I think perhaps a better way to put what I interepret to be your point would be "the globally optimal amount of local optimization is locally suboptimal".

This is further complicated by some people overestimating their ability to optimize things, and underestimating the costs (in time, but also psychological) of doing that optimization. For example, I worked in a company that had lots of meetings to make sure that we were doing the higher-priority task instead of a lower-priority task, but I often suspected that if we didn't have these meetings, we would simply have enough time to do both. Maybe in the wrong order, but the overall outcome would still be better.