Via integer linear programming (with a binary decision variable for each positive integer up to a specified bound), I found optimal solutions for $2n\in\{6,8,\dots,24\}$:
\begin{matrix}
2n & \text{solution} \\
\hline
6 & \{14,15,20,21,22,33\} \\
8 & \{2,3,4,8,9,15,16,21\} \\
10 & \{5,25,26,34,35,38,39,51,55,57\} \\
12 & \{3,5,9,16,25,27,34,35,38,39,46,55\} \\
14 & \{5,7,25,49,58,62,69,77,85,87,91,92,93,95\} \\
16 & \{2,3,5,7,8,9,25,32,49,58,69,77,81,85,91,95\} \\
18 & \{2,7,8,11,32,49,87,93,111,121,133,142,143,145,155,161,185,187\} \\
20 & \{2,4,5,7,9,11,16,25,27,49,58,111,121,123,125,133,143,155,161,187\} \\
22 & \{3,5,9,49,82,106,125,164,177,187,203,209,212,215,217,219,221,235,247,253,259,299\} \\
24 & \{5,7,9,11,13,49,94,118,121,123,125,169,183,201,215,217,221,236,247,253,259,265,278,319
\}
\end{matrix}
By request, here's the SAS code I used:
proc optmodel;
num n = 3;
num m = 50;
set NODES = 1..m;
set EDGES = {i in NODES, j in NODES: i < j and gcd(i,j) > 1};
var UseNode {NODES} binary;
var UseEdge {EDGES} >= 0 <= 1;
min Objective = max {i in NODES} i * UseNode[i];
/* var MinMax;*/
/* min Objective = MinMax;*/
/* con MinMaxCon {i in NODES}:*/
/* MinMax >= i * UseNode[i];*/
con Cardinality:
sum {i in NODES} UseNode[i] = 2*n;
con DegreeThree {k in NODES}:
sum {<i,j> in EDGES: k in {i,j}} UseEdge[i,j] = 3 * UseNode[k];
con NodesImplyEdge {<i,j> in EDGES}:
UseNode[i] + UseNode[j] - 1 <= UseEdge[i,j];
solve linearize;
print UseNode;
quit;
If your solver doesn't support automatic linearization, you can instead use the commented code to manually linearize the min-max objective.