As long as the attribute and level lists are the same, then you could use relative strength of design (relative D-efficiency) to compare situations involving designs differing in terms of sample size, #tasks, and #concepts. It just depends on the type of comparisons you are wanting to make.
For example, either doubling the sample size or doubling the tasks should lead to a doubling of relative D-efficiency...since we are doubling the information.
But, doubling the number of concepts shown per task does not double the relative D-efficiency.
Typically, researchers hold the sample size, #tasks, and #concepts per task constant so they can observe the differences in D-efficiency due to other issues (such as prohibitions).
And, remember, gains in D-efficiency don't necessarily equate to improvements in the final utilities! D-efficiency assumes people answer like logit-based robots. Real humans often don't answer according to logit (additive rule, with exponentiated utilities proportional to choice likelihoods). Plus, real humans often tire out and have limitations to information processing (e.g. real humans don't necessarily have full resilliance if doubling the tasks or doubling the concepts shown per task).