I am trying to develop an advertising strategy to maximize ticket sales for the Seattle Mariners baseball team. To help me out I am using a machine learning model where the input is an advertising strategy $($e$.$g$.$ how much advertising to purchase on TV vs$.$ Radio vs$.$ Billboards etc. and where to to distribute$),$ and the output is the expected change in tickets sold over the entire season. I may have gone a little overboard, because now I have over $10$ million different strategies to compare!

I know that the best strategy will result in a increase of $400,000$ tickets, and the most expensive one will cost $$800,000.$

To help me pick the best one, I want to have $2$ lists of strategies $-$ one sorted by cost and the other sorted by increase in sales

Should I sort using Quick Sort, Insertion Sort, Heap Sort, or Radix Sort?

Suggested algorithm: Choose $1$ of $4$:

$-$Quick Sort

$-$Insertion Sort

$-$Heap Sort

$-$Radix Sort

Relevant Properties: $($Choose $1$ or more$)$

Choice $1$ of $4$:Adaptive

Choice $2$ of $4$:Stable

Choice $3$ of $4$:In$-$place

Choice $4$ of $4$:non$-$comparison based

Justification: