How many data points for test set in a time series I have a monthly sales data set from 2018 January onwards. I would like to know from expert what is the optimum train test split and minimum train test split. Also to mention that my data includes 2020 year data where the sales have been influenced due to pandemic, and 2021 have been recovering year.

I am not aware of any theoretical work giving minimum or optimum train/test set splits for time series, and I doubt that such general guidelines could be given with any theoretical foundations.
Sales data frequently exhibit seasonality. So one reasonable split would be to hold out the entire last year as a test set, which still gives you more than two years to fit your models on (a good thing) and still allows you to analyze whether accuracy was systematically better or worse during certain parts of the year.
And as you write, the last two years have seen a big impact from COVID. I would say that any conclusions of the kind "method A outperformed method B in the test set, therefore we will use A henceforth" will be far more driven by COVID idiosyncrasies than by optimal or suboptimal train/test splits.
