The idea here is that there's no silver bullet, for what you must consider what are the types of tasks being executed, and try to schedule them as nicely as possible.
CPU-bound tasks don't use much communication (I/O), and thus, need to be continuously executed, and interrupted only when necessary -- according to the policy being used;
I/O-bound tasks may be continuously put aside in the execution, allowing other processes to work, since it will be sleeping
for many periods, waiting for data to be retrieved to primary memory;
interative tasks must be continuously executed, but needs not to be executed without interruptions, as it will generate interruptions, waiting for user inputs, but it needs to have a high priority, in order not to let the user notice delays in the execution.
Considering this, and the context switch costs, you must evaluate what types of tasks you have, choosing, thus, one or more policies for your scheduler.
Edit:
I thought this was a simply conceptual question. Considering you have to implement a solution, you must analyze the requirements.
Since you have the length of the tasks, and the context switch times, and you have to maintain the cores busy, this becomes an optimization problem, where you must keep the minimal number of cores idle when it reaches the end of the processes, but you need to maintain the minimum number of context switches, so that your overall execution time does not grow too much.
As pointed by svick, this sounds like a partition problem, which is NP-complete, and in which you need to divide a sequence of numbers into a given number of lists, so that the sum of each list is equal to each other.
In your problem you'd have a relaxation on the objective, so that you no longer need all the cores to execute the same amount of time, but you want the difference between any two cores execution time to be as small as possible.
In the reference given by svick, you can see a dynamic programming approach that you may be able to map onto your problem.