The scaling behaviour of randomly branched polymers in a good solvent is studied in two to nine dimensions, using as microscopic models lattice animals and lattice trees on simple hypercubic lattices. As a stochastic sampling method we use a biased sequential sampling algorithm with re-sampling, similar to the pruned-enriched Rosenbluth method (PERM) used extensively for linear polymers. Essentially we start simulating percolation clusters (either site or bond), re-weigh them according to the animal (tree) ensemble, and prune or branch the further growth according to a heuristic fitness function. In contrast to previous applications of PERM, this fitness function is not the weight with which the actual configuration would contribute to the partition sum, but is closely related to it. We obtain high statistics of animals with up to several thousand sites in all dimension 2 ≤ d ≤ 9. In addition to the partition sum (number of different animals) we estimate gyration radii and numbers of perimeter sites. In all dimensions we verify the Parisi–Sourlas prediction, and we verify all exactly known critical exponents in dimensions 2, 3, 4 and ≥8. In addition, we present the hitherto most precise estimates for growth constants in d ≥ 3. For clusters with one site attached to an attractive surface, we verify for d ≥ 3 the superuniversality of the cross-over exponent at the adsorption transition predicted by Janssen and Lyssy, but not for d = 2. There, we find = 0.480(4) instead of the conjectured = 1/2. Finally, we discuss the collapse of animals and trees, arguing that our present version of the algorithm is also efficient for some of the models studied in this context, but showing that it is not very efficient for the 'classical' model for collapsing animals.