If you can install GNU Parallel on Windows under Git Bash (ref), then you can run the two scripts on separate CPUs this way:
▶ (cat <<EOF) | parallel --jobs 2
python script1.py
python script2.py
EOF
Note from the parallel man page:
--jobs N
Number of jobslots on each machine. Run up to N jobs in parallel.
0 means as many as possible. Default is 100% which will run one job per
CPU on each machine.
Note that the question has been updated to state that parallelisation does not improve calculation time, which is not generally a correct statement.
While the benefits of parallelisation are highly machine- and workload-dependent, parallelisation significantly improves the processing time of CPU-bound processes on multi-core computers.
Here is a demonstration based on calculating 50,000 digits of Pi using Spigot's algorithm (code) on my quad-core MacBook Pro:
Single task (52s):
▶ time python3 spigot.py
...
python3 spigot.py 52.73s user 0.32s system 98% cpu 53.857 total
Running the same computation twice in GNU parallel (74s):
▶ (cat <<EOF) | time parallel --jobs 2
python3 spigot.py
python3 spigot.py
EOF
...
parallel --jobs 2 74.19s user 0.48s system 196% cpu 37.923 total
Of course this is on a system that is busy running an operating system and all my other apps, so it doesn't halve the processing time, but it is a big improvement all the same.
See also this related Stack Overflow answer.